Cortico-cortical potentials mapping of the face recognition network

Summary form only give. Face recognition can be defined as the ability of a system to classify or describe a human face. The motivation for such system is to enable computers to do things like humans do and to apply computers to solve problems that involve analysis and classification. Face recognition systems require less user cooperation than systems based on other biometrics (e.g. fingerprints and iris), it is one of the most widely investigated biometric techniques for human identification and it can be used in applications such as access control, passport control, surveillance, criminal justice and human computer interaction. Face recognition is a specific case of object recognition. It is not a unique and rigid object. Indeed, Global features are sensitive to variations caused by emotional expressions, illumination, pose and occlusions. Neural networks have been widely used for applications related to face recognition and Backpropagation Neural Network (BPNN) is one of the most widely used methods in this domain. In this paper we present 3 solutions related to neural network for color face recognition. First we introduce learning-based dimension reduction algorithms. In the literature many methods are used to reduce the dimensionality of the subspace in which faces are presented. Recently, Random Projection (RP) has emerged as a powerful method for dimensionality reduction. It represents a computationally simple and efficient method that preserves the structure of the data without introducing very significant distortion. Our focus was to investigate the dimensionality reduction offered by RP and perform an artificial intelligent system for face recognition. According to the experimental results, we conclude that random projection is an optimal method of dimensionality reduction. In the case of our study, obtaining a higher FR rate depends, among others, on the choice of the random projection matrix and the dimension of the feature vector of original data. Secondly, we propose a hybrid method to achieve face recognition purpose using semi supervised BPNN. Traditionally, BPNN needs supervised training to learn how to predict results from desired data, the idea of our approach is to get the desired output of the network from an exterior classifier (SOM) and then apply the back propagation algorithm to recognize facial data. Experiments show that the results are satisfying in comparison with the supervised BPNN. Furthermore, we can deduce that the unlabeled vector in the training DB generally does not influence the recognition task and due to its generation ability the neural net can even correct some misclassified vectors. The third study concerns the use of Bhattacharyya distance to calculate the total error of the network. The error function generally used to train the neural network is Mean Square Error (MSE) based on Euclidean distance measure. In the experimental section we compare how the algorithm converge using the Mean Square Error and the Bhataccharyya distance and results indicated that the image faces can be recognized by the proposed system effectively and swiftly.

Face Recognition is a prototype with stirring functions in the direction of facial features for consent and validation. In this approach, a particular type of face recognition systems is bond different feature extraction approaches to conquer the image entity configuration. To solve face recognition under unreliable pose is rigorous pose invariant features with effective approach. In this work, an algorithm is implemented in MATLAB and performance evaluation of achievement rate with existing techniques is proposed. Also, the performance of the face recognition system is based on feature extractions using wavelet transform and demonstrates the improved quality image. This work provides Artificial Neural Network (ANN) for face recognition and using a combination of Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) algorithm for feature extraction and acquires a transformed face image to raise the features. The results demonstrated that the proposed system has higher accuracy, precision and sensitivity than face recognition using different feature extractors.

Human face recognition is one of the most important research areas in biometrics. However, the robust face recognition under a drastic change of the facial pose, expression, and illumination is a big challenging problem for its practical application. Such variations make face recognition more difficult. In this paper, we propose a novel face recognition method, called Attentional Feature-pair Relation Network (AFRN), which represents the face by the relevant pairs of local appearance block features with their attention scores. The AFRN represents the face by all possible pairs of the 9x9 local appearance block features, the importance of each pair is considered by the attention map that is obtained from the low-rank bilinear pooling, and each pair is weighted by its corresponding attention score. To increase the accuracy, we select top-K pairs of local appearance block features as relevant facial information and drop the remaining irrelevant. The weighted top-K pairs are propagated to extract the joint feature-pair relation by using bilinear attention network. In experiments, we show the effectiveness of the proposed AFRN and achieve the outstanding performance in the 1:1 face verification and 1:N face identification tasks compared to existing state-of-the-art methods on the challenging LFW, YTF, CALFW, CPLFW, CFP, AgeDB, IJB-A, IJB-B, and IJB-C datasets.

Application of PCA Based Neural Network in Face Recognition

Although the face recognition method for in-depth learning has high accuracy, the model is complex and the recognition speed is slow. In order to realize real-time face recognition of students while learning video, a real-time face recognition method based on Dlib is proposed. In this essay, based on the real-time face recognition method, Dlib is used as a face recognition tool, and the Dlib environment is configured. A face detection method based on the Dlib toolkit is designed. That is, using the convolution neural network method (CNN) and the deep residual network (ResNet) for real-time face recognition, and finally using the GPU to accelerate the operation of the face feature extraction network, sharing the CPU load to improve the overall operating efficiency of the system. By numerous repeated experiments and tests, it shows that the accuracy of face recognition is high and has certain application value.

To improve our understanding of Svalbard-type polythermal glacier drainage, hydraulic geometry models of the subglacial hydrology of two contrasting glaciers in Svalbard have been constructed. The models are tested against a uniquely long and rich set of field observations spanning 45 years. Digital elevation models (DEMs) were constructed from bedrock data measured with ground penetrating radar and surface data of two medium-sized polythermal glaciers, Hansbreen and Werenskioldbreen, in south-west Spitsbergen. Hansbreen has a low angle bed with over-deepenings and a calving front, while Werenskioldbreen has steeper bed and terminates on land. Together they are representative of many Svalbard glaciers. The DEMs were used to derive maps of hydraulic potential and subglacial drainage networks. Validation of the models was done using field observations including location mapping and speleological exploration of active moulins, positions of main river outflows, dyetracing and water chemistry studies, and observations of water pressure inside moulins. Results suggest that the water pressure is generally close to ice overburden pressure but varies greatly depending on local conditions such as bed location, the thickness of cold ice layer, the thickness of the glacier and seasonal changes in meltwater input.

Over the past several years, the performance of state-of-the-art face recognition systems has been significantly improved, due in a large part to the increasing amount of available face datasets and the proliferation of deep neural networks. This rapid increase in performance has left existing popular performance evaluation protocols, such as standard LFW, nearly saturated and has motivated the emergence of new, more challenging protocols (aimed specifically towards unconstrained face recognition). In this work, we employ the use of parts-based face recognition models to further improve the performance of state-of-the-art face recognition systems as evaluated by both the LFW protocol, and the newer, more challenging protocols (BLUFR, IJB-A, and IJB-B). In particular, we employ spatial transformers to automatically localize discriminative facial parts which enables us to build an end-to-end network where global features and local features are fused together, making the final feature representation more discriminative. Experimental results, using these discriminative features, on the BLUFR, IJB-A and IJB-B protocols, show that the proposed approach is able to boost performance of state-of-the-art face recognition systems. The pro-posed approach is not limited to one architecture but can also be applied to other face recognition networks.

Face detection by low-resolution image (LR) is one of the key aspects of Human-Computer Interaction(HCI). Due to the LR image, which has changes in pose, lighting, and illumination, the performance of face recognition is reduced. In this work, we propose the Deep Belief Network-Crossover based Firefly (DBN-CROFF) method for face recognition from low-resolution images. The Histogram of Gradient (HOG) and 2-Dimensional Discrete Wavelet Transform (2D-DWT) to extract facial width, size of the cheeks, skin tone, nose, and lip shape features from facial data. The Kernel Principle Component Analysis (k-PCA) is used to successfully reduce the dimension of the feature. The experimental performance of the proposed method is evaluated using four datasets namely LFW, Multi-PIE, Extended Yale-B, and FERET with conventional techniques. Finally, the proposed DBN-CROFF solution surpasses the other conventional facial recognition approaches by giving a higher accuracy of recognization.

This chapter is concerned with face recognition based on videos or, more generally, sets of images, using deep learning techniques. We first briefly review some naive yet commonly used strategies pertaining to using frame-level features extracted by deep convolutional neural networks (CNNs) for video-level face recognition. Representative strategies include naive feature pooling and pairwise feature distance computation. Then, we present a method named neural aggregation network (NAN), which is a deep learning framework tailored for video-based representation and recognition. NAN can automatically learn the quality of faces in a video/image set and aggregate the frame-level deep features accordingly, yielding more discriminative video-level features. We conduct experimental evaluation on three video face recognition datasets. The results indicate that while previous deep learning-based methods with naive pooling or pairwise distances have obtained substantial improvements over traditional methods, the NAN method further outperforms them by an appreciable margin.

Face recognition in low-resolution images has seen significant advancements over the past few decades. Although extensive research has been conducted to improve accuracy in these conditions, one of the main challenges remains the difficulty in identifying unique facial features in low-resolution images, leading to high error rates in identification. The use of Deep Convolutional Neural Networks (DCNN) for low-resolution face recognition is still limited. However, employing super-resolution models like REAL-ESRGAN can enhance recognition accuracy in low-resolution images. This study utilizes the Light CNN architecture and applies the margin-based identity loss function AdaFace on low-resolution datasets. The model is trained using the Casia-WebFace dataset and evaluated using the LFW and TinyFace test datasets. Based on the evaluation results on the LFW test data, the best model is Light CNN9-AdaFace, achieving the highest accuracy of 97.78% at 128x128 resolution. For images with the lowest resolution of 16x16, an accuracy of 83.37% was achieved using super-resolution techniques. On the TinyFace test data, the use of super-resolution resulted in performance metrics with a Rank-1 accuracy of 47.26%, Rank-5 accuracy of 55.25%, Rank-10 accuracy of 58.61%, and Rank-20 accuracy of 61.90% using the Light CNN9-AdaFace architecture.

A Gabor-based network for heterogeneous face recognition

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.

Attention-based techniques have been successfully used for rating image quality, and have been widely employed for set-based face recognition. Nevertheless, for video face recognition, where the base convolutional neural network (CNN) trained on large-scale data already provides discriminative features, fusing features with only predicted quality scores to generate representation are likely to cause duplicate sample dominant problem, and degrade performance correspondingly. To resolve the problem mentioned above, we propose a redundancy removing aggregation network (RRAN) for video face recognition. Compared with other quality-aware aggregation schemes, RRAN can take advantage of similarity information to tackle the noise introduced by redundant video frames. By leveraging metric learning, RRAN introduces a distance calibration scheme to align distance distributions of negative pairs of different video representations, which improves the accuracy under a uniform threshold. A series of experiments is conducted on multiple realistic data sets to evaluate the performance of RRAN, including YouTube Faces, IJB-A, and IJB-C. In comprehensive experiments, we demonstrate that our method can diminish the overall influence of poor quality components with large proportion in the video and further improve the overall recognition performance with individual difference. Specifically, RRAN achieves a 96.84% accuracy on YouTube Face, outperforming all existing aggregation schemes.

Recent research in convolutional neural network (CNN) has provided a variety of new architectures for deep learning. One interesting new architecture is the local binary convolutional neural network (LBCNN), which has shown to provide significant reduction in the number of parameters to be learned at training. In this paper, we study the influence of network parameters in the scenario of face recognition, comparing LBCNN against other famous networks available in the literature in terms of sensibility and processing time. In our study, we also propose a pre-processing step on images to increase the accuracy of the model, besides investigating its behaviour with noisy images. Our experiments are carried on the Chokepoint dataset, whose face subimages were collected from video frames under real-world surveillance conditions, including variations in terms of illumination, sharpness, pose, and misalignment due to automatic face detection. The conclusion is that by using the Laplacian step and a reduced amount of LBC modules, it is possible to train LBCNN more quickly and with improved accuracy. In addition, it was found that LBCNN is very sensitive to noise and better results can be achieved when noisy images are inserted in the training set.

As an active research topic, many algorithms have been presented for face recognition. However, they mainly utilize the monochromatic intensity information. Among a few color face recognition methods, most of them treat the three channels separately. In this paper, a color face image is treated in a holistic manner by using the quaternion theory. We then propose a new algorithm for color face recognition, which uses the quaternion Zernike moment invariants and the quaternion BP neural network for the color face recognition. Experimental results on the Collection of Facial Images (Grimace) database, including major expression variation and considerable variation in head turn and tilt, show that the proposed method is better than the conventional ones in recognition rate.