Comparative Analysis of Machine Learning Based Approaches for Face Detection and Recognition

Introduction

In current scenario face detection and recognition is very complicated and challenging problem in Pattern Analysis, Computer Vision, Neural Networks and Machine Learning. This problem is discussed in various learning communities such as controller environment and uncontrolled environment.  The facial applications are accepting 2D facial images and acquired different facial descriptors to uses different learning techniques. In recent development deep learning has found a new approach for researchers in artificial intelligence in face detection and recognition. It is finding of the objective goals and it is very popular rapidly growing technique to use in face detection and recognition. It has developed to solve the complex problem of machine learning. It has worked on human and computers related problem. Deep learning is an advance idea and technique based concept to detect the faces for identification and verification. In deep learning the computer likes as learners and performs the classification task directly to the next video and images. It is solving the state of art problem and improves the accuracy and reliability of human. Deep learning has bounded and trained labeled data in neural network architecture. That contains many numbers of layers in convolution neural network. Hidden layer has worked as a bridge between input and output layers. They are working for simple and complex shape of images. It solves the identification and verification problems using maximum number of layers which are found in hidden layer. The first introduction of deep learning and theory is produced in 1980s. The following reasons they have become a powerful concept for feature extraction using in face detection and recognition.

Convolutional Neural Network

It is the best concept of machine learning. This has been used in Pattern analysis, Computer vision and Machine learning. Convolution neural network is a best technique to analyse the previous knowledge or data for solved the future problem. It is depending on the transform an input image to convert in output image using multiple hidden layers. It is associated with machine learning and pattern analysis. The researchers are focusing on identification and verification problem of the images and videos. They have found a lot of solutions to solve the problem of face detection and recognition. In deep learning solution the term deep is refers to the concept of numbers of hidden layers present in max pooling networks. Generally they are two to three layers, but deep networks analysis hundreds of layers. While deep learning is working on face detection and recognition, it achieved the higher accuracy. They can help using their expectations. Deep learning is working as a robot because this is including artificial intelligence. It can learn their features with the help of feature in hidden layers.

In Figure 1 is showing architecture of convolution neural network from their relationship between input, output and hidden layers. Neural network architecture is organizing in the layers of consistency in a set of interconnected node. Which are known as hidden layers? They are classified the feature of the input images and mapped into the stored images. Hidden layer is occupied maximum number of layers regarding users. These have classified different-different features in simple and complex image in datasets. Figure 1 represents the architecture model of convolution neural network to including in the combination between input, hidden and output layers. These layers are the most important part of the convolution neural network, because they are reading all the features of the input images and classifying according their features in hidden layers. Then organize the output of the input images.

Machine Learning Methods in Face Detection and Recognition Algorithms

It has been used for large number of labeled data present in datasets. That is matching pattern of features directly involved in datasets and using without manual feature extractions. A deep neural network is combination of non-linear processing layers using in machine learning and pattern analysis. They have interconnected with multiple layers like that input layer for input data, hidden layer for features and output layers for object. Deep learning is solving the problem of faces in the real time images and databases. They are working on the collection of images and normalized the networks. Deep learning has a latest idea and concept to improve the face related problem in real time environment.

Alex Net

Alex Net is the first deep learning architecture which was introduced in 1980s by Geoffery Hinton and his colleagues. It is very popular and simple model for researchers to solve the problem in face detection and recognition. It has a simple architecture to combination of convolutional layer and max pooling layers. In deep neural network there is a combination of convolutional layer, multiple pooling and fully connected layer. They have collected bunch of information from the input and the filtered it in convolution layer including different type of hidden layers. So there are various categories of input object. In figure 2 deep neural network collects the input image and identifies or categories the object. They categorize the object and give the efficient outputs. Deep convolution neural network is working in different layers. Convolution layer contains the input image through a set of transform neural network. These transform learning and recognize the features of the images. Pooling layer is simplifying the output by performing non-linear reducing, down sampling the number of dimensions for using about the features of images. Fully connected layer is playing a role to recognize the object. After completing the feature detection in the deep convolution network they will go to next layer called fully connected layer.

Feed forward Neural Network

It has most popular algorithm in deep learning concept. It will work on the feature of the faces in machine learning. It has worked to identify the features from input layers. They are not using manual feature extraction. So they have reduced the time for identifying features in. It has solved and classified the features in databases. Figure 3 Shows architecture model feed forward network containing both weight and bias features. They are also containing the three layer concept, so it has assigned multiple neural networks. In this model h is known has hidden layers shown in figure 3. They have performed voi (output layer), h has hidden layer and woi as bias layers. Bias has assigned weights in network model. Figure 3 architecture of feed forward network is found three layers. There are proving a good result to communicate between collections of layers. The input layers x1 to xn have connected to the hidden layer h1 to hn layers. Those have connected to output layers y1 to yn and communicated and interconnected with weight and bias in network model. They are flowing in reverse direction. Computational complexity is depending on the amount of hidden layers. They are performing high accuracy in network models. The bias is more effective in working with input and output layers.

Deep Convolution Neural Network

It has communicated non-linear model using maximum number layers. It has operated the faces and recognizes using machine learning. This network has assigning multiple hidden layers for used multiple feature of the faces. These layers have worked with neurons or nodes. DCNN has processed the set of objects and after processed, they have automatically recognized the object face to corresponding input images. In DCNN the labelled data worked has a training data in datasets. It has been used to understand the images and matched these features in matching categories in datasets. DCNN has transferred the previous data to collecting features in next layers of the architecture. DCNN has improved the complexity and accuracy of the object. They are working on the pattern from layer to layer in DCNN.

K-nearest Neighbors

KNN algorithm is one of the best algorithms for introducing the feature of nearest neighbor or nodes. This is also known as lazy algorithms. K means the number of iterations. This algorithm is using simple to understand and communicate to each other’s. K-nearest neighbor algorithm is working on nearest node of features of the object. So it has worked as a non-parametric algorithm. The term non-parametric is not making any assumptions to underlying data distribution. This has been used real word databases system. Maximum practical data does not follow the typical theoretical analysis in feature learning process. KNN algorithm is assuming the features space in nearest feature in the database.

Principal Component Analysis

It has a very popular and oldest technique in the image processing, and pattern analysis. This has developed by Pearson (1901) and improved by Hotlling (1933). It has worked on the Eigen value and Eigen vectors using matrix form. It has used different applications with different variety. The concept of PCA has to reduce the dimensionality features of databases. It is capable to large number of datasets. Their features have interrelated to remaining possible databases in the system.

Linear Discriminant Analysis

This method has similar as fisher discriminant analysis. It is used to describe the images including local features. These features have worked in the form of pixel value. They have following defined as known shape feature, texture feature and color features. It has identified the features to use between   linear separating vectors. And also have used similar feature in the image. These procedures have been used to maximize between class scatter and intra class variance in face detection and recognition. 

Feature Extraction

It has a very popular concept used to extract the features from images in face detection and recognition. It has been widely used in different approaches, such as Digital Image processing, Pattern Classification, Computer Vision, and Deep Learning. It has transformed the input materials or images in pixel. This pixel value has transformed a combination of features in database. Because these selected features are containing most appropriate information in the original data. It is very useful in biometrics applications and Machine Learning.

Geometric Based Methods in Feature Extraction

Geometric features based approaches is computing a set of faces such as a mouth, eyes, ears and nose. In this geometric representation shows the position of eyes, mouth, ear and nose is a form of feature vectors. They are reliable to detect the automatic feature extraction and significant for face detection and recognition. Geometric feature are representing the shape, location and color of the facial components, which are extracting a feature component for recognizing the faces.

Holistic Based Methods in Feature Extraction

It has most useful technique in face detection and recognition. They are using feature description methods based appearance in face detection and recognition. Holistic used feature extraction in for any local extraction method is a form of information of data and reduced a typical transformation that describes a large data from images in the database. Holistic based feature extraction is converting the image into a low- dimensional feature space with improving discriminant power of the faces.

Related work of Machine Learning Approaches in Face Detection and Recognition

In literature survey from various researchers, they have proposed the machine learning approaches. They have found several solutions and techniques for detection of faces and recognizing it. (Lee, Won, & Hong, 2020) proposed assembled ResNet-50 reveals increase in top-1 accuracy from 76.3 per cent to 82.78 per cent, mCE from 76.0 per cent to 48.9 per cent and mFR from 57.7 per cent to 32.3 per cent of ILSVRC2012 validation collection. (Bau, Zhu, Strobelt, Lapedriza, Zhou, & Torralba, 2020) has studied a convolution neural network (CNN) that is specialized in the classification of scenes and discover units that fits a complex range of object concepts. (Liu, et al., 2020) launched a FinRL DRL library that will make it easier for beginners to invest in quantitative finance and develop their own stock trading strategies. (Carion, Massa, Kirillov, & Zagoruyko, 2020) proposed key components of the new system, called DEtection TRansformer or DETR, are a set-based global loss that forces specific predictions by bipartite matching, and transforming encoder-decoder architecture. In view of Fixed limited collection of learned object questions, DETR explanations for object relations, and global image meaning for direct production.

(Agarap, 2019) proved that in the case as CNN-SVM achieved test accuracy of 90.72 per cent, while CNN-Softmax achieved test accuracy of 91.86 per cent. (Chopade, Edwards, Khan, & Pu, 2019, November) proposed explicitly interested in how teamwork skills and team interactions are demonstrated as verbal and non-verbal actions, and how these behaviours can be recorded and evaluated by passive data collection of F. (Trabelsi, Chaabane, & Ben-Hur, 2019) worked on deepRAM, an end-to-end deep learning platform that provides for the implementation of novel and previously proposed architectures; its fully automated model selection process allows for a rational and impartial evaluation of deep learning architectures. A work (Bali, Kumar, & Gangwar, 2019) proposed a speed of wind speed weather forecasting to calculate the wind speed to using deep learning technique. They are using different approaches to provide and solution of wind speed weather forecasting. (Howard, et al., 2019) created a model and generalized and extended to object recognition and semantic segmentation tasks. They suggest a new effective segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling for the role of semantic segmentation (or any dense pixel prediction) (LR-ASPP). (Shao, Zhang, & Fu, 2017) proposed a Sparse many to one encoder (SMF) and collaborative random faces (RFs). They have worked on pose invariant face representation and detect the faces. Author is working on different paper using Multi PIE pose datasets; you tube datasets (YTF) and real world datasets. They have improved the performance from 7 to 14% in face detection and recognition. (Tsai, Li, Hsu, Qian, & Lin, 2018) proposed an unsupervised learning framework and joint optimization framework. This technique has enhancing co-segmentation mask for improving the co-saliency features. Unsupervised learning and joint optimization framework is exploring the concept of the objectness and saliency in different type of multiple images or datasets Cosal2015, iCoseg, Image Pair and MSRC datasets are providing high quality result on both co-saliency and co-segmentation.

Hu, Cho, Wang, & Yang (2014) proposed a Non-blind deconvolution method to suppress the ringing artifacts caust by light for face detection and recognition. Non-blind deconvolution method is detecting light stretches for corrupted images and incorporates it into an optimizing framework for face detection and recognition. The author has worked on png and jpeg images using low light environment. (Anwar, 2018) proposed to deblurring for class specific problem and class genetic blind deconvolution. This proposed method has used to overcome the limitation of existing method. When dealing with blurred image lacking high frequency. This is focusing only bulrred images containing a single object and class specific training with using CMUPIE, CAR, FTHZ and INRIA datasets. (Wang, Ma, Chen, & You, 2017) proposed RegionNET or RexNet and Salient Object detection techniques to solve the problem in face detection and recognition. RexNet has provided saliency mapping between end to end with shape object boundaries for VGG, ImageNet, ContexNet, ECSSD, DOTOMRON and RGBD1000 datasets. A RexNet has worked on a detection and multi scale conceptual robustness technique in face detection and recognition. (Deng, Hu, & Guo, 2018) proposed an image filtering binarization and spatial histogram for face detection and recognition. The author has developed scattering Compressive Binary Pattern (SCBP) descriptor to improve the performance of face image. SCBP is using handcraft by 6RF Eigen filters to achieved accurate and robust performance. CBP is also used to improving the robustness of LBP. In this paper authors used to DFD and CBFD for derived noise sensitive filtering adapting to fine grained structure for FERET, LPW and PaSC databases in Face detection and recognition. Koteswar Rao et.al. (2018)  used co-saliency estimation method in different datasets for face detection and recognition. Co-saliency estimation method used simple scale estimation application as demonstrated on the large scale ImageNet, MSRC, iCoseg and Coseg-Rep datasets. This method is solving map problem with well separated background and foreground. This framework is able to achieve very competitive results. (Tulyakov, Jeni, Cohn, & Sebe, 2017) proposed a regression forest algorithm for detecting the problem of face detection and recognition. They have solved the problem in efficient manner with consistency. These have robust in 3D face rotation of MultiPIE, HBPD, BU-4DFE and MultiPIE-VC datasets. These approaches are finding effective face pose estimation and viewpoint consistency on multiple measurements. This method is performing highly competitive score on a range of benchmarks. (Dong, Zheng, Ma, Yang, & Meng, 2018) proposed a Multi model and  Self-Paced Learning Algorithms for Detection (MSPLD) and few example object detection (FEOD) for face detection and recognition. This model has used a large number of pools in unlabeled image and using a few labeled images per category. This is being used for discriminating knowledge for different detection model. This method is giving better result in PASCAL-VOC2007, PASCAL-VOC2012, MSCOCO2014, ILSURC2013 and ImageNet-COCO datasets.

(Mafi, Rajaei, Cabrerizo, & Adjouadi, 2018) proposed a Switching based Adaptive Median and Fixed based Weighted Mean Filter (SAMFWMF) for face detection and recognition. SAMFWMF has controlled the similar edge detection and sharping in Lena (5128512), Cameraman (250*250), Coins (300*246) and Checkboard (256*256) images. SAMFWMF is performing better structural metrics. They are solving better result in contract to other common thresholding method in detecting the faces and then recognizing it. (Tao, Guo, Li, & Gao, 2017) proposed a model of Tensor Rank Preserving Discriminant Analysis (TRPDA) technique to solve the problem of face detection and recognition. They have performed robust performance and produced the high rate in UMIST, ORL and CAS-PEAL-R1 datasets. TRPDA has extracted the feature with the rank information and elimination.  They are usable manifold learning method in face detection and recognition. (Wang, Yan, Cui, Feng, Yan, & Sebe, 2018) proposed recurrent face aging (RFA) and RNN in face detection and recognition. RFA has improved 65.43% and the accuracy of bilayer has 61%. It shows RFA works slighting better than bilayer RNN. The author is using LFW and CACD datasets for better performance in face detection and recognition. RFA framework consists of triple layer GRU, There are giving better performance of the identity information in bilayer GRU for face detection and recognition.  (Jeong, Lee, Kim, Kim, & Noh, 2017) used Markov random field energy optimization. This method has especially worked as wide base line multi view environment. This segmentation method has improved the performance with similar quality. That has produced in the current state of art models. They have captured features in various critical conditions. They have included maximum number of rotations, views and distance between captured by cameras. A sparse wide baseline has captured very efficiently. (Sagonas, Ververas, Panagakis, & Zafeiriou, 2017) proposed Joint and Individual Variance Explained (JIVF) and Robust JIVE (RJPVE) in face detection and recognition. It has improved the accuracy of faces. It has also identified information of the faces remain in the RJIVE based progression of FG-NET datasets. The accuracy is depending on pair of images converted to age difference. (Zhu, Liu, Lei, & Li, 2017)  worked on a 3D Dense Alignment (3DDFA) and 3D Morphable model (3DMM) in face detection and recognition. Face alignment has covered with full pose range. It has achieved poses variation in face alignment of ALFW, AFW, LFPW, HELEN, IBUG, 300W and AFLW 2003D datasets.  Comparing performance the drop brought is replacing boundary poses. This method demonstrates best robustness initialization 3DDFA person in face detection and recognition. (Wang, Yan, Cui, Feng, Yan, & Sebe, 2018) used one to more different techniques. That has been used in face detection and recognition. (Ding, Xu, & Tao, 2015) proposed Controlled Face Feature (CPF) in face detection and recognition. Using CPF in faces of extensive experiments shows the demonstrate superiority in both learning representation and rotating frontal images. Face recognition experiment on MultiPIE database is providing more evidence that illustrate the task of strength in specific methods. (Qian, Deng, & Hu, 2018, May) proposed a facial action unit in face detection and recognition. Facial action unit has derived to solve the identification problem using comprehensive computer vision algorithms. These datasets are DISFA and AM-FED used for color features. This color can also be used to detect of action unit activation. (Cruz, Foi, Katkovnik, & Egiazarian, 2018) proposed a single image super resolution (SISR) and CNN for face detection and recognition. 1D wiener filtering is working on similarity domains. They are giving effective solution for specific problem of SISR. These results is sharper reconstruction and in SET5, SET14 and Urban datasets. This method works well only on image substantial self-similarity. (Wang, Ma, Chen, & You, 2017) proposed an algorithm to solve the cross age face verification for face problem in face detection and recognition. These have comparatively worked on effective balance between feature share and feature exclusion. (Malhotra, Bali, & Paliwal, 2017, January) proposed to solve the problem of cryptography and network security to using intrusion detection system. These techniques have solved and implement the problem of machine learning very frequently. (Duong, Quach, Luu, N., & Savvides, 2017, October) proposed a Temporal Non-Volume Preserving (TNVP) and Generative Adversarial Network (GAN) for face detection and recognition using FGNET, MORPH, CACD and AGFW datasets. TNVP has evaluated both terms of synthesizing progressed faces of ages and cross face verification age with consistency. TNVP has guaranteed attractable density function. They have extracted features information and inference the value of consecutive stage of faces in evaluation of embedded datasets. (He, Wu, Sun, & Tan, 2017, February) proposed visual verses infrared (VSS-NIR) and invariant deep representation (IDR) using CASIA, NIR-VIS2.0 and Large Scale VIS datasets for face detection and recognition. They are achieving 94% verification rate in large scale VIS data comparing with state of art. This is reducing the error rate of 58% only with a compact 64 D representation.

Summary Sheet of Machine Learning Approaches for Face Detection and Face Recognition 

Table 1 shows the summary of the machine learning approaches for face detection and recognition in images and videos. Table 1 also presents all the methods of face detection and recognition by the various technique and datasets with results. Table 1 also shows the merit and demerit of the technique by using various authors.

Table 1. Summary sheet of technique, authors, datasets, results, merit and demerits of the machine learning approaches in face detection and recognition

Observation and Recommendation of Machine Learning Approaches in Face Detection and Recognition

Face detection and recognition applications are using on 2D dimensional face images. They need large number of feature matching in different techniques. Using learning applications, they have improved the accuracy of face images in datasets. The impact factor of pose, illumination and expression is the basic and complete information about the face images. Face identification and verification is very important factor of the unknown persons. The moving object is very challenging problem in face identification and also find another problem in aging and non-rigid motion of the object. Learning discriminant appearances in face representation they are depend on invariant poses in face detection and recognition. Face identification is a critical issue in face recognition systems. This problem is identifying to change of poses to the object in different angles. So it is comparing the database between test face image and registered face images.

In face detection and recognition are using collaborative random faces (RFs) guided encoder to recognize the facial appearances between the test faces and registered faces.  Random features are matching the pattern of faces used in database. They are using three types of feature appearances learning techniques.

1. Supervised learning is the process of providing the series of sample faces and comparing with the resultant faces with the expected outcomes. In a face detection and recognition are recognizing in supervised learning.
2. Unsupervised learning is more complex and difficult to implement the database. Because after using unsupervised learning algorithms the target resultant faces are unknown. Unsupervised learning is also called self-organizing and self-learning. Unsupervised learning process extracts the statistical properties of the training image and group’s similar vector into features.

Reinforcement learning, the output images are not recognizing after comparing parallel output images. In this technique teacher is present but they are confused about answers. For high level identity feature are used to supervise auto encoder. In this technique, Identify the similar feature value from output features value after extracting to identify the features, then they are improving the accuracy and reliability of the face using different-different datasets and algorithms.

Features of the faces and there level of alignment are managed to discriminative identity features and explore them. The face identification of the common structure is the same value of the pose, illumination and extraction but their identities are different. It means they have registered same faces on different identity so it is big problem to identify the true value of the image. In this situation feature learning is done important role to find the true value of the images.

In figure 4 and Figure 5 is showing different technology used in to identify the deep feature of the faces. This concept is arising in 1998. In 1998 LetNet was very popular technique; they are basically depending on the neurons. But in 1998 to till 2020 there were very popular and many researchers used and develop different-different technologies to identify the features of the faces. In current scenario there are all most 96.76% is solved out. But still there are many problems are identified in our surrounding, so it is very important and useful to improve that technology as soon as possible. Machine learning based approaches work on the various problems related to pattern analysis, to detect the facial expression and face recognition. 

Conclusions

This article discusses a photos-based device that enables users to use the webcam to recognise and detect many face-related features. In this article, we are performing comprehensive and systemic study to check the efficacy of these classic representation learning structures on class-imbalanced outcomes. We also show that deeper discrimination can be learned by creating a deep network that retains inter-cluster differences both within and within groups. MobileNet, which provides both offline and real-time precision and speed to provide fast and consistent stable results, is the recently suggested Convolutional Neural network (CNN) model. This paper has evaluated the comprehensive survey of machine learning based approaches in face detection and recognition including various techniques and datasets. This paper also discussed the summary of related work presented by various researchers in face detection and recognition using different techniques. This paper also discussed the integration of machine learning based approach with multiple image related dataset and improving the performance of the faces using classifier.