Improve Face Recognition Performance Research Articles

Advancing Face Recognition Technologies: The Role of Decision Trees in Classifying Complex Image Pairs

The advancement of face recognition technologies has been pivotal in various applications, from security systems to personalized user experiences. There are significant efforts already devoted to solving challenges of multimodality and pose variation in face recognition. Some studies focus on multimodality but pose-invariant, and other studies focus on pose variation but single modality. Despite significant progress, various face recognition algorithms do not consider both multimodality and pose variation constraints in their proposed methods. Recognizing face images presented both in a different modality and in a different pose presents serious challenges to current algorithms. This paper proposes an algorithm that combines the strengths of deep learning with decision trees to improve face recognition performance across modalities and poses in constrained and unconstrained environments. This hybrid approach leverages the representational power of deep learning and the interpretability and simplicity of decision trees. The findings indicate significant improvements over existing methodologies, particularly in challenging conditions like when multimodality and pose variation constraints are compounded together in the input face images in both constrained and unconstrained environments. The proposed algorithm not only addresses the limitations of current face recognition systems but also offers scalable, efficient solutions suitable for real-world applications.

Literature review on the application of numerical model in improving face recognition performance

As an important biometric technology, the applications of face recognition technology have been applied in many fields. With the development of related technology, the application of different algorithms has improved the face recognition technology in different situations. This paper mainly reviews three mathematical models of face recognition based on neural networks, namely genetic algorithm and artificial neural network (GA-ANN), principal component analysis method and feed-forward neural networks (PCA-FNN) and Hidden Markov Models (HMMs) and State-Action-Reward-State-Action (SARSA). In this paper, three models are introduced and analysed in detail. Through analysis, it has been concluded that the GA-ANN improves classification accuracy, the PCA-FNN is used in face recognition with constant posture and the use of HMM-SARSA improves the accuracy of face recognition.

A Deep Transfer Learning Approach for Addressing Yaw Pose Variation to Improve Face Recognition Performance

A Deep Transfer Learning Approach for Addressing Yaw Pose Variation to Improve Face Recognition Performance

Enhancing eID card mobile-based authentication through 3D facial reconstruction

In recent years the number of credentials and digital identities needed to access online services is exploding, increasing the risk of fraud and unauthorized access to personal data.Biometric verification techniques can help to build more secure authentication systems; in particular face recognition algorithms have reached near perfect accuracy under ideal conditions. In Europe 60% of population owns an electronic identification (eID) document which contains biometric data, and almost all member states provide tools to verify the identity of a user who holds an eID card; however, the adoption of biometric authentication schemes is still limited. Reasons for this low adoption are privacy concerns, about the storage or transmission of personal images, and the degradation of performance of facial recognition algorithms with 2D images acquired "in the wild" (real world condition with inconsistency in lighting and positioning, unclear or occluded facial features, noise from the background). In this paper a new authentication scheme is proposed, exploiting images acquired "in the wild" and 3D facial reconstruction techniques which can help in improving the quality of the probed images, as changes in lighting or position have limited impact on facial shape. Several experiments have been conducted in order to investigate the performance of the proposed method, using the Bosphorus and the NIST Face Recognition Grand Challenge (FRGC) datasets. In particular, one of the most accurate ML face recognition algorithms has been selected to compare the performance of 2D snapshot data, obtained from 3D back-projection, against 2D uncontrolled images acquired "in the wild". We show that using 3D facial reconstructions significantly improve face recognition performance, allowing for a more secure and robust authentication system.

Fast and reliable probabilistic face embeddings based on constrained data uncertainty estimation

Probabilistic Face Embeddings (PFE) can improve face recognition performance in unconstrained scenarios by integrating data uncertainty into the feature representation. However, the matching speed of PFE is too slow to be applied to large-scale face recognition or retrieval applications. Moreover, since deep learning-based data uncertainty estimation tends to be over-confident, the recognition performance of PFE is unstable. This paper proposes a probabilistic face embedding method to improve the robustness and speed of PFE. Specifically, the mutual likelihood score (MLS) metric used in PFE is simplified by using a one-dimensional variance to approximate the data uncertainty of face feature, to speed up the matching of probabilistic embedding pairs. Then, a unilateral constraint loss is proposed to limit the variation range of the lower part of the estimated data uncertainties, which can solve the problem of accuracy degradation in high-quality images. In addition, a feature fusion method based on temperature scaling is proposed, which can adjust the fusion weights of different quality images to improve the performance of video face recognition. Comprehensive experiments show that the proposed method can achieve comparable or better results in 6 benchmarks than the state-of-the-art methods with a less computational cost at the matching process. The code of our work is publicly available in GitHub (https://github.com/KaenChan/ProbFace).

Do portrait artists have enhanced face processing abilities? Evidence from hidden Markov modeling of eye movements

Recent research has suggested the importance of part-based information in face recognition in addition to global, whole-face information. Nevertheless, face drawing experience was reported to enhance selective attention to the eyes but did not improve face recognition performance, leading to speculations about limited plasticity in adult face recognition. Here we examined the mechanism underlying the limited advantage of face drawing experience in face recognition through the Eye Movement analysis with Hidden Markov Models (EMHMM) approach. We found that portrait artists showed more eyes-focused eye movement patterns and outperformed novices in face matching, and participants' drawing rating was correlated with both eye movement pattern and performance. In contrast, portrait artists did not outperform novices and did not differ from novices in eye movement pattern in either the face recognition or part-whole tasks, although the eyes-focused pattern was associated with better recognition performance and longer response times in the whole condition relative to the part condition. Interestingly, in contrast to the face recognition and part-whole tasks, participants' performance in face matching was predicted by their drawing rating but not eye movement pattern. These results suggested that artists' advantage in face processing is specific to tasks similar to their drawing experience such as face matching, and may be related to their better ability in extracting identity-invariant information between two faces rather than more eyes-focused eye movement patterns.

Student Attendance Monitoring System Using Face Recognition

There is no reason that a critical educational practise like attendance should be viewed in the old, tedious manner in this age of rapidly evolving new technologies. In the conventional method, it is difficult to manage large groups of students in a classroom. Since it takes time and has a high risk of error when entering data into a system, it is not recommended. Real-Time Face Recognition is a practical method for dealing with a large number of students' attendance on a daily basis. Many algorithms and techniques have been developed to improve face recognition performance, but our proposed model employs the Haarcascade classifier to determine the to determine the positive and negative characteristics of the face, as well as the LBPH (Local binary pattern histogram) algorithm for face recognition, all of which are implemented in Python and the OpenCV library. For user interface purposes, we use the tkinter GUI interface.

APA: Adaptive Pose Alignment for Pose-Invariant Face Recognition

Face alignment is a crucial step in face recognition. Through making the position of face consistent, face alignment reduces intra-class variability due to factors such as lighting, background, pose, and perspective transformation, and further facilitating the recognition tasks. In this paper, we propose a new face alignment method for pose-invariant face recognition, called adaptive pose alignment (APA), which can greatly reduce the intra-class difference and correct the noise caused by the traditional method in the alignment process, especially in unconstrained settings. Instead of aligning all faces to the pre-defined, uniform frontal shape, we adaptively learn the alignment templates according to the facial poses and then align each face of training or testing sets to its related template. To further improve face recognition performance, we propose a simple, yet effective feature normalization method which can generate more discriminative feature representation of a face or a set of faces (template) combined with the APA method. Furthermore, we introduce a pose-invariant face recognition pipeline that sequentially applies APA-based alignment, deep representation by softmax or ArcFace loss function, and the effective feature normalization procedure. We empirically show that APA-based images can accelerate the training of deep face recognition model by aligning all the images to the optimal templates. Moreover, experimental results show that the proposed method achieves the state-of-the-art performance on popular challenging face datasets including IJB-A, IJB-C, and CPLFW datasets.

Hellinger Distance Decision Tree (HDDT) Classification of Gender with Imbalance Statistical Face Features

Face recognition is one of the technologies used for assets protection. Face recognition also presents a challenging problem in the field of image and computer vision and has been used for the application such as face tracking and personal identification. It also frequently used in a security system such as a security camera in airport, banks, and offices. Practically, there are problems in improving face recognition performance, particularly for gender identification. It is very difficult to differentiate the person based on face appearance from different poses, lighting, expressions, aging and illumination. Sometimes it is also difficult to identify the shape of human faces because different people have a different structure of faces. This study used image retrieved from Student Information Management Systems (SIMS)from 10 male and 43 female students who're taking MAT530. The image was then generated 12 geometric landmarks using TI nspire software. The main goal of this research is to classify the gender through the images of faces and to resolve for imbalance data using Hellinger Distance Decision Tree (HDDT) classifier. This classifier was proposed as an alternative to decision tree technique which used Hellinger Distance as the splitting criteria. The result from the validation split shows that percentage split at 40% produced the highest value of accuracy rate at 77.2727% and has the most significant value of sensitivity and specificity.

A joint loss function for deep face recognition

Convolutional neural networks (CNNs) have been widely used in computer vision community, and significantly improving the state-of-the-art. How to train an intra-class variant and inter-class discriminative feature is a central topic in face recognition. This paper proposes to learn an effective feature from face images by a joint loss function which combines the hard sample triplet (HST) and the absolute constraint triplet (ACT) loss, under the criteria that a maximum intra-class distance should be smaller than any inter-class distance. With the joint supervision of HST and ACT loss, CNNs is enable to learn discriminative features to improve face recognition performance. Experiments on labeled faces in the wild, IARPA Janus Benchmark (IJB-A) and YouTube Faces datasets achieve a comparable or superior performance to the state-of-the-arts.

Radial basis function neural network-based face recognition using firefly algorithm

This paper presents an adaptive technique for obtaining centers of the hidden layer neurons of radial basis function neural network (RBFNN) for face recognition. The proposed technique uses firefly algorithm to obtain natural sub-clusters of training face images formed due to variations in pose, illumination, expression and occlusion, etc. Movement of fireflies in a hyper-dimensional input space is controlled by tuning the parameter gamma (γ) of firefly algorithm which plays an important role in maintaining the trade-off between effective search space exploration, firefly convergence, overall computational time and the recognition accuracy. The proposed technique is novel as it combines the advantages of evolutionary firefly algorithm and RBFNN in adaptive evolution of number and centers of hidden neurons. The strength of the proposed technique lies in its fast convergence, improved face recognition performance, reduced feature selection overhead and algorithm stability. The proposed technique is validated using benchmark face databases, namely ORL, Yale, AR and LFW. The average face recognition accuracies achieved using proposed algorithm for the above face databases outperform some of the existing techniques in face recognition.

Multimodal 2D–3D face recognition using local descriptors: pyramidal shape map and structural context

In this study, the authors propose a local descriptor based multimodal approach to improve face recognition performance. Pre-processing is done to smooth, resample, and register data. The resampled three-dimensional (3D) face data are applied to extract novel descriptors including pyramidal shape index, pyramidal curvedness, pyramidal mean, and Gaussian curvatures. Proposed pyramidal shape maps are extracted at each level of the Gaussian pyramid on each point of the 3D data to have 2D matrices as representatives of 3D geometry information. A local descriptor structural context histogram, which represents the structure of the image using scale invariant feature transform, is calculated on pyramidal shape map descriptors and texture image to find matched keypoints in 3D and 2D modality, respectively. Score-level fusion by means of sum rule is employed to get a final matching score. Experimental results on the Face Recognition Grand Challenge (FRGC v2) database illustrate verification rates of 99 and 98.65% at 0.1% false acceptance rate for all versus all and ROC III experiments, respectively. On Bosphorus database, verification rate of 95.8% for neutral versus all experiment has been achieved.

Effective face recognition using bag of features with additive kernels

In past decades, many techniques have been used to improve face recognition performance. The most common and well-studied ways are to use the whole face image to build a subspace based on the reduction of dimensionality. Differing from methods above, we consider face recognition as an image classification problem. The face images of the same person are considered to fall into the same category. Each category and each face image could be both represented by a simple pyramid histogram. Spatial dense scale-invariant feature transform features and bag of features method are used to build categories and face representations. In an effort to make the method more efficient, a linear support vector machine solver, Pegasos, is used for the classification in the kernel space with additive kernels instead of nonlinear SVMs. Our experimental results demonstrate that the proposed method can achieve very high recognition accuracy on the ORL, YALE, and FERET databases.

Face recognition using a novel image representation scheme and multi-scale local features

This paper presents a new method for improving face recognition performance under difficult conditions. Specifically, a new image representation scheme is proposed which is derived from the YCrQ colour space using principal component analysis PCA followed by Fisher linear discriminant analysis FLDA. A multi-scale local feature, LBP-DWT, is used for face representation which is computed by extracting different resolution local binary patterns LBP features from the new image representation and transforming the LBP features into the wavelet domain using discrete wavelet transform DWT and Haar wavelets. A variant of non-parametric discriminant analysis NDA, called regularised non-parametric discriminant analysis RNDA is introduced to extract the most discriminating features from LBP-DWT. The proposed methodology has been evaluated using two challenging face databases FERET and multi-PIE. The promising experimental results show that the proposed method outperforms two state-of-the-art methods, one based on Gabor features and the other based on sparse representation classification SRC.

Recognition based on two separated singular value decomposition-enriched faces

In previous studies on human face recognition, illumination pretreatment has been considered to be among the most crucial steps. We propose the illumination compensation algorithm two separated singular value decomposition (TSVD). TSVD consists of two parts, namely the division of high- and low-level images and singular value decomposition, which are implemented according to self-adapted illumination compensation to resolve the problems associated with strong variation of light and to improve face recognition performance. The mean color values of the three color channels R, G, and B are used as the thresholds, and two subimages of two types of light levels are then input with the division of the maximal mean and minimal mean, which are incorporated with light templates at various horizontal levels. The dynamic compensation coefficient is proportionately adjusted to reconstruct the subimages. Finally, two subimages are integrated to achieve illumination compensation. In addition, we combined TSVD and the projection color space (PCS) to design a new method for converting the color space called the two-level PCS. Experimental results demonstrated the efficiency of our proposed method. The proposed method not only makes the skin color of facial images appear softer but also substantially improves the accuracy of face recognition, even in facial images that were taken under conditions of lateral light or exhibit variations in posture.

Face hallucination and recognition in social network services

Due to the rapid growth of social network services such as Facebook and Twitter, incorporation of face recognition in these large-scale web services is attracting much attention in both academia and industry. The major problem in such applications is that visual data provided are in general noisy, low resolution, prone to degradation due to lighting and other adverse effects. This paper proposes a novel face hallucination method with an aim of improving face recognition performance with the photography in social media data, which is modeled as a progressive process which explores the specific face characteristics and priors based on frequency bands analysis. In the first stage of our algorithm, initially estimated middle-resolution images are generated based on a patch-based learning method in discrete cosine transformation (DCT) domain as the first-scale restoration image. According to the relationship between the high-resolution face images and their lower resolution ones, the DC coefficients and AC coefficients are estimated separately. In the second stage, aiming at generating more refined high-resolution face images, a DCT up-sample algorithm emphasizing on low-frequency bands preserving is applied. Meanwhile, an interpolation-based method is presented in spatial domain to obtain high-frequency bands. Extensive experiments show that the proposed algorithm effectively helps improving the recognition performance of low-resolution faces.

Normalization Method of Eye Location for Improving Face Recognition Performance

Normalization Method of Eye Location for Improving Face Recognition Performance

Depth and Intensity Gabor Features Based 3D Face Recognition Using Symbolic LDA and AdaBoost

In this paper, the objective is to investigate what contributions depth and intensity informat ion make to the solution of face recognition problem when expression and pose variations are taken into account, and a novel system is proposed for combin ing depth and intensity information in order to improve face recognition performance. In the proposed approach, local features based on Gabor wavelets are extracted fro m depth and intensity images, wh ich are obtained fro m 3D data after fine align ment. Then a novel hierarch ical selecting scheme embedded in symbolic linear discriminant analysis (Symbolic LDA) with AdaBoost learning is proposed to select the most effective and robust features and to construct a strong classifier. Experiments are performed on the three datasets, namely, Texas 3D face database, Bhosphorus 3D face database and CASIA 3D face database, which contain face images with comp lex variations, including expressions, poses and longtime lapses between two scans. The experimental results demonstrate the enhanced effectiveness in the performance of the proposed method. Since most of the design processes are performed automatically, the proposed approach leads to a potential prototype design of an automat ic face recognition system based on the combination o f the depth and intensity informat ion in face images.

3D Face Recognition Based on Depth and Intensity Gabor Features using Symbolic PCA and AdaBoost

Traditional 2D face recognition based on optical (intensity or color) images faces many challenges, such as illumination, expression, and pose variation. In fact, the human face generates not only 2D texture information but also 3D shape information. In this paper, the objective is to investigate what contributions depth and intensity information make to the solution of face recognition problem when expression and pose variations are taken into account, and a novel system is proposed for combining depth and intensity information in order to improve face recognition performance. In the proposed approach, local features based on Gabor wavelets are extracted from depth and intensity images, which are obtained from 3D data after fine alignment. Then a novel hierarchical selecting scheme embedded in symbolic principal component analysis (Symbolic PCA) and AdaBoost learning is proposed to select the most effective and most robust features and to construct a strong classifier. Experiments are performed on the three datasets, namely,Texas 3D face database, Bhosphorus 3D face database and CASIA 3D face database, which contain face images with complex variations, including expressions, poses and long time lapses between two scans. The experimental results demonstrate the enhanced effectiveness in the performance of the proposed method. Since most of the design processes are performed automatically, the proposed approach leads to a potential prototype design of an automatic face recognition system based on the combination of the depth and intensity information in face images.

Discriminant analysis and similarity measure

The cosine similarity measure is often applied after discriminant analysis in pattern recognition. This paper first analyzes why the cosine similarity is preferred by establishing the connection between the cosine similarity based decision rule in the discriminant analysis framework and the Bayes decision rule for minimum error. The paper then investigates the challenges inherent of the cosine similarity and presents a new similarity that overcomes these challenges. The contributions of the paper are thus three-fold. First, the application of the cosine similarity after discriminant analysis is discovered to have its theoretical roots in the Bayes decision rule. Second, some inherent problems of the cosine similarity such as its inadequacy in addressing distance and angular measures are discussed. Finally, a new similarity measure, which overcomes the problems by integrating the absolute value of the angular measure and the lp norm (the distance measure), is presented to enhance pattern recognition performance. The effectiveness of the proposed new similarity measure in the discriminant analysis framework is evaluated using a large scale, grand challenge problem, namely, the Face Recognition Grand Challenge (FRGC) problem. Experimental results using 36,818 FRGC images on the most challenging FRGC experiment, the FRGC Experiment 4, show that the new similarity measure improves face recognition performance upon other popular similarity measures, such as the cosine similarity measure, the normalized correlation, and the Euclidean distance measure.