Retracted: Modeling of Feature Based MCA Classifier of LBP-HOG-Statistical-Wavelet Transform

In this study, we present the performance of Support Vector Machine (SVM), Convolution Neural Network (CNN), and Artificial Neural Network (ANN) with Bag of Words (BoW), Histogram of Oriented Gradients (HOG), and Image Pixels (IP) for face recognition. SVM, CNN, and ANN are machine learning approaches and has been used for pattern recognition, especially in face recognition technology. BoW, HOG, and IP are being used for image feature extraction. The testing has been conducted from publicly available AT&T face database. Every individual subject consists of 10 images with different facial expression, different illumination and the dimensions of the images is unified as 92-by-112 pixels with PGM formats. SVM achieved recognition accuracy of 97.00%, 96.00%, and 98.00% with subsequently BoW, HOG, and IP. CNN achieved recognition accuracy of 94.00%, 99.00%, and 99.50% with subsequently BoW, HOG, and IP. ANN achieved recognition accuracy of 96.00%, 99.00%, and 99.50% with subsequently BoW, HOG, and IP. The experimental results show that the IP with ANN approach clearly outperformed the others approaches.

Extraction of global face appearance and local interior differences is essential for any face recognition application. This paper presents a novel framework for face recognition by combining two effective descriptors namely, Zernike moments (ZM) and histogram of oriented gradients (HOG). ZMs are global descriptors that are invariant to image rotation, noise and scale. HOGs capture local details and are robust to illumination changes. Fusion of these two descriptors combines the merits of both local and global approaches and is effective against diverse variations present in face images. Further, as the processing time of HOG features is high owing to its large dimensionality, so, the study proposes to improve its performance by selecting only most discriminative HOG features (named discriminative HOG (DHOG)) for performing recognition. Efficacy of the proposed methods (DHOG, [Formula: see text] and [Formula: see text]) is tested on ORL, Yale and FERET databases. DHOG provides an improvement of 3% to 5% over the existing HOG approach. Recognition results achieved by [Formula: see text] and [Formula: see text] are up to 15% and 18% higher respectively than those obtained with these descriptors individually. Performance is also analyzed on LFW face database and compared with recent and state-of-the-art methods.

Brain inspired face recognition: A computational framework

Face recognition is a process of verifying an individual using facial images and it is widely employed in identifying people on social media platforms, validating identity at ATMs, finding missing persons, controlling access to sensitive areas, finding lost pets, etc. Face recognition is still a trending research area because of various challenges like illumination variations, different poses, and expressions of the person. Here, a novel methodology is introduced for face recognition using Histogram of Oriented Gradients (HOG), histogram of Local Binary Patterns (LBP), and Convolutional Neural Network (CNN). The features from HOG, histogram of LBP, and deep features from the proposed CNN are linearly concatenated to produce the feature space and then classified by Support Vector Machine. The face databases ORL, Extended Yale B, and CMUPIE are used for experimental work and attained a recognition rate of 98.48%, 97.33%, and 97.28% respectively.

Modern face recognition systems leverage datasets containing images of hundreds of thousands of specific individuals' faces to train deep convolutional neural networks to learn an embedding space that maps an arbitrary individual's face to a vector representation of their identity. The performance of a face recognition system in face verification (1:1) and face identification (1:N) tasks is directly related to the ability of an embedding space to discriminate between identities. Recently, there has been significant public scrutiny into the source and privacy implications of large-scale face recognition training datasets such as MS-Celeb-1M and MegaFace, as many people are uncomfortable with their face being used to train dual-use technologies that can enable mass surveillance. However, the impact of an individual's inclusion in training data on a derived system's ability to recognize them has not previously been studied. In this work, we audit ArcFace, a state-of-the-art, open source face recognition system, in a large-scale face identification experiment with more than one million distractor images. We find a Rank-1 face identification accuracy of 79.71% for individuals present in the model's training data and an accuracy of 75.73% for those not present. This modest difference in accuracy demonstrates that face recognition systems using deep learning work better for individuals they are trained on, which has serious privacy implications when one considers all major open source face recognition training datasets do not obtain informed consent from individuals during their collection.

Face Recognition plays a very important role in numerous occasions based on visual security in recent days. The current methods of face recognition are to extract the different features of different faces to distinguish from others, so feature extraction has become a vital step in face recognition. For this reason, this paper presents a new fusion of local difference binary (LDB) and histogram of oriented gradients (HOG) for face recognition. We use LDB descriptor to extract the local pattern features of a face image. At the same time, the edge features of the original image are extracted by using HOG descriptor. The proposed new fusion of features improves the shortcomings of the low accuracy and avoids the problems that the dimension of the general fusion of features is too high. The experimental results on ORL and Yale face database verify the validity of the proposed fusion of features.

Face recognition is efficiently used in computer vision, pattern recognition, biometric, law enforcement, surveillance, criminal investigations, and missing person detection. However, face recognition is still a challenging task under variant illuminations, pose, expression, occlusion, and background. In this paper, we proposed a cascading of histogram of oriented gradients (HOG) and local binary pattern (LBP) feature extraction method on ORL face dataset, which can improve recognition rates while also addressing pose, scale, expression, and variant illumination issues. The facial recognition network was trained using various classifiers such as with KNN, SVM, and random forest (RF). The results have been shown that the accuracy rate of the combined LBP and HOG feature extraction method is better than individual LBP or HOG features for ORL database face recognition.

Face recognition in biometrics is a difficult process because of factors including individual differences in facial emotions, illumination, resolution, facial rotation, and facial structure similarities. "A Combination of Cross Domain Features for Face Recognition" is what we suggest here. The benchmarked face images are considered, downsized to consistent 128x128 dimensions, and converted to grayscale. Face photos are compressed, and their quality is improved by using the Discrete Wavelet Transform (DWT). Initial characteristics that are unaffected by changes in an image's lighting are extracted using the Histogram of Oriented Gradients (HOG) on a compressed LL band of DWT. By connecting the LL band's output to the HOG's input, the DWT and HOG's LL bands are connected in a cascade. HOG's input and output are combined to create new, strong final features. The system's results when comparing test and database features are calculated using the Euclidean Distance (ED). It has been noted that the suggested system outperforms the current ones in terms of results.

In this paper, a new method for facial expression classification is proposed, which uses the histograms of oriented gradients (HOG) algorithm to extract facial expression features and the sparse representation classifier (SRC) to classify facial expressions with a large variation of poses. The HOG algorithm was selected due to its effectiveness in picking up both local and global facial expression features in different orientations and scales, and the SRC was chosen because of its proven effectiveness in face recognition. A novelty of the proposed approach is that given a facial image for classification, its pose is determined first to select a pose-dependent dictionary for the SRC procedure. The paper also discusses ways of selecting parameters for improving the effectiveness of the HOG algorithm. The proposed method was applied to two multi-pose facial expression databases and satisfactory results were obtained for the majority of facial expressions under various poses.

Efficient face descriptors require a careful equilibration between accuracy and feature dimension. In recent years Histogram of Oriented Gradient (HOG) starts to be used in the face recognition task. However the best recognition rate for HOG requires a high dimensional feature. In this paper, we will incorporate fuzzy concept to HOG aiming to achieve a good recognition rate with a low feature vector dimension. The proposed Histogram of Fuzzy Oriented Gradient is applied to the face recognition task. Experimental results on ORL database have demonstrated that HFOG outperforms the original HOG with a lower dimensional vector.

In the era of smart digital transformation, automatic face recognition is the way of identification and verification of a person in many applications of security and authentication. If the person's 2D still image or 3D video frame is taken under controlled lighting and frontal face poses, then today automatic face recognition is a solved problem with more than 98 % accuracy. Under ideal condition of images, automatic face recognition outperforms manual face recognition rate. But the problem of uncontrolled illumination, occlusion, tilting of faces, different expressions of faces, use of accessories and hair color, growth of facial hair, aging effect of a person and low-resolution images makes automatic face recognition underperform; it gets defeated by human. Unless this problem is solved on real time, automatic face recognition system cannot be trusted for crucial security applications like e-passport, fraud detection, counter terrorism and mug-shot verification etc. Extensive research is underway to improve this technology. Traditionally Harr cascade classifier methods, histogram of oriented gradients (HoG), principal component analysis (PCA), Eigen-faces, and support vector machine (SVM) classifier are used in face recognition. Now modern deep learning innovation has superseded those traditional machine learning techniques with more computing power in GPUs and TPUs to tackle all variations in input image quality and ever-growing face database. The goal of this paper is to report the journey of face recognition system from traditional to modern techniques to increase precision and recall of the system.

It is estimated that 10% to 20% of road accidents are related to fatigue, with accidents caused by drowsiness up to twice as deadly as those caused by other factors. In order to reduce these numbers, strategies such as advertising campaigns, the implementation of driving recorders in vehicles used for road transport of goods and passengers, or the use of drowsiness detection systems in cars have been implemented. Within the scope of the latter area, the technologies used are diverse. They can be based on the measurement of signals such as steering wheel movement, vehicle position on the road, or driver monitoring. Driver monitoring is a technology that has been exploited little so far and can be implemented in many different approaches. This work addresses the evaluation of a multidimensional drowsiness index based on the recording of facial expressions, gaze direction, and head position and studies the feasibility of its implementation in a low-cost electronic package. Specifically, the aim is to determine the driver's state by monitoring their facial expressions, such as the frequency of blinking, yawning, eye-opening, gaze direction, and head position. For this purpose, an algorithm capable of detecting drowsiness has been developed. Two approaches are compared: Facial recognition based on Haar features and facial recognition based on Histograms of Oriented Gradients (HOG). The implementation has been carried out on a Raspberry Pi, a low-cost device that allows the creation of a prototype that can detect drowsiness and interact with peripherals such as cameras or speakers. The results show that the proposed multi-index methodology performs better in detecting drowsiness than algorithms based on one-index detection.

This paper presents experimental results related to a simplified and efficient face recognition system using a basic webcam. More specifically, the purpose of this work is two-fold: 1) to detect constantly the individual's face in front of the computer, in an uncontrolled environment; 2) to send an alert to the system manager if another individual's face is recognised. Sixteen distances based on 14 points of the face between eyes, nose and mouth are considered. Experimental results carried out on two different training sets are presented. The first database has been constructed on 500 real face images of ten individuals (50 faces each one), while the second database has been created on the same 500 previous images and 500 new synthetic data obtained through a crossover operation. For experimental evaluation, a 5-NN classifier is used. Finally, results show the performance in terms of a new correlation score for the recognition task.

The current national standards of face recognition do not put the placement of light for a clear description, only said that the light should be uniform when face recognition, no overexposure and underexposure. In this paper, we present a novel database and use different face recognition algorithms to determine the placement of light during face recognition. For this study, we have chosen ten widely-used face recognition algorithms: principal component analysis(PCA), linear discriminant analysis(LDA), independent component analysis(ICA), nonnegative matrix factorization (NMF), histogram of oriented gradient(HOG), scale-invariant feature transform(SIFT), face recognition, CosFace, ArcFace, and Combined Margin. We use a novel database that contains face images with a wide range of illumination angles to analyze the relationship between these algorithms and illumination angles. The results of this experiment indicate that the horizontal illumination angle of the light should be controlled in the range from -10° to 10° and the vertical illumination angle should be controlled in the range from -10° to 10°.

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.