Elastic Bunch Graph Matching Based Face Recognition Under Varying Lighting, Pose, and Expression Conditions

This research aims to develop artificial intelligence (AI) algorithms in the context of facial recognition with a focus on increasing accuracy in difficult environmental conditions. Although facial recognition technology has made great progress, challenges such as poor lighting, variations in facial expressions, and head rotation are still problems that must be overcome. The research methodology involved collecting a wide dataset covering a wide variety of faces under various environmental conditions. This data is then processed and its features are extracted using computer image processing techniques. Furthermore, several deep neural network architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), were developed, trained, and evaluated for face recognition tasks. The expected result is the development of an AI algorithm that is able to overcome challenges in facial recognition with higher accuracy than existing methods. In particular, significant improvements in facial recognition accuracy are expected especially under low lighting conditions and variations in facial expressions. This research has a major impact in a variety of security applications, such as border surveillance, building access control, and corporate security. With higher facial recognition accuracy, security risks can be significantly reduced, resulting in safer and more efficient security solutions. In conclusion, this research aims to bring innovation in facial recognition technology through advanced AI approaches, with the potential to improve security in various contexts.

Face recognition becomes a challenging problem in real-world surveillance videos where the low-resolution probe frames exhibit variations in pose, lighting condition, and facial expressions. This is in contrast with the gallery images which are generally frontal view faces acquired under controlled environments. A direct matching of probe images with gallery data often leads to poor recognition accuracy due to the significant discrepancy between the two kinds of data. In addition, the artifacts such as low resolution, blurriness and noise further enlarge this discrepancy. In this paper, we propose a video based face recognition framework using a novel image representation called warped average face (WAF). The WAFs are generated in two stages: in-sequence warping and frontal view warping. The WAFs can be easily used with various feature descriptors or classifiers. As compared to the original probe data, the image quality of the WAFs is significantly better and the appearance difference between the WAFs and the gallery data is suppressed. Given a probe sequence, only a few WAFs need to be generated for the recognition purpose. We test the proposed method on the ChokePoint dataset and our in-house dataset of surveillance quality. Experiments show that with the new image representation, the recognition accuracy can be boosted significantly.

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.

Compared to more conventional techniques such as fingerprint recognition, the use of biometric authentication systems in computer-based examinations offers several advantages. To overcome the disadvantages of fingerprint biometrics, such as high administrative costs, long authentication times, and accuracy issues, this paper proposes an innovative solution that leverages state-of-the- art facial recognition technology. Although biometric fingerprint systems are reliable to a certain extent, they present significant challenges in the context of computer-assisted examinations. The authentication process can be time-consuming and can result in delays and logistical problems. These systems can also be prone to errors, including false positives and false negatives, compromising the integrity of the investigation process. These limitations require research into more efficient and accurate biometric solutions. The developed system uses FaceNet and Multi- Task Cascaded Convolutional Neural Network (MTCNN) algorithms in achieving superior accuracy, efficiency, as well as security in verifying candidates' identities during exams. FaceNet excels at facial recognition by mapping faces in a compact Euclidean space, ensuring high accuracy even with slight variations in facial expressions, angles or lighting conditions. MTCNN increases the robustness of the system through precise face detection and alignment, which are critical for reliable performance. The results show that the facial recognition system outperforms traditional fingerprint-based methods. Accuracy is significantly improved, reducing misidentifications, while streamlining the authentication process, minimizing delays, and improving overall efficiency. The robustness of the system ensures consistent performance despite environmental fluctuations.

An efficient face recognition system should recognize faces in different views and poses. The efficiency of a human face recognition system depends on the capability of face recognition in presence of changes in the appearance of face due to expression, pose and illumination. A novel algorithm which utilizes the combination of texture and depth information based on Modular PCA to overcome the problem of pose variation and illumination change for face recognition is proposed. The system has combined 2D and 3D systems in the feature level which presents higher performance in contrast with methods which utilizes either 2D or 3D system separately. A multimodal face recognition based on Modular PCA when compared with conventional PCA algorithm has an improved recognition rate for face images with large variations in illumination and facial expression. The proposed algorithm is tested with FRAV3D database that has faces with pose variation and illumination changes. Recognition rates from experimental results show the superiority of Modular PCA over conventional PCA methods in tackling face images with different pose variations and changes in illuminations. The proposed algorithm shows a recognition rate of 86% that is achieved in fusion experiment.

Face recognition (FR) is a practical application of pattern recognition (PR) and remains a compelling topic in the study of computer vision. However, in real-world FR systems, interferences in images, including illumination condition, occlusion, facial expression and pose variation, make the recognition task challenging. This study explored the impact of those interferences on FR performance and attempted to alleviate it by taking face symmetry into account. A novel and robust FR method was proposed by combining multi-mirror symmetry with local binary pattern (LBP), namely multi-mirror local binary pattern (MMLBP). To enhance FR performance with various interferences, the MMLBP can 1) adaptively compensate lighting under heterogeneous lighting conditions, and 2) generate extracted image features that are much closer to those under well-controlled conditions (i.e., frontal facial images without expression). Therefore, in contrast with the later variations of LBP, the symmetrical singular value decomposition representation (SSVDR) algorithm utilizing the facial symmetry and a state-of-art non-LBP method, the MMLBP method is shown to successfully handle various image interferences that are common in FR applications without preprocessing operation and a large number of training images. The proposed method was validated with four public data sets. According to our analysis, the MMLBP method was demonstrated to achieve robust performance regardless of image interferences.

In this paper, a robust face recognition algorithm is proposed, which is based on the elastic graph matching (EGM) and discriminative feature analysis algorithm. We introduce a cost function for the EGM taking account of variations in face pose and facial expressions, and propose its optimization procedure. Our proposed cost function uses a set of Gabor-wavelet-based features, called robust jet, which are robust against the variations. The robust jet is defined in terms of discrete Fourier transform coefficients of Gabor coefficients. To cope with the difference between face poses of test face and reference faces, 2 x 2 warping matrix is incorporated in the proposed cost function. For the discriminative feature analysis, linear projection discriminant analysis and kernel-based projection discriminant analysis are introduced. These methods are motivated to solve the small-size problem of training samples. The basic idea of PDA is that a class is represented by a subspace spanned by some training samples of the class instead of using sample mean vector, that the distance from a pattern to a class is defined by using the error vector between the pattern and its projection to the subspace representing the class, and that an optimum feature selection rule is developed using the distance concept in a similar way as in the conventional linear discriminant analysis. In order to evaluate the performance of our face recognition algorithm, we carried out some experiments using the well-known FERET face database, and compared the performance with recently developed approaches. We observed that our algorithm outperformed the compared approaches.

A lot of higher-order computer vision applications, like face recognition and facial expression analysis, depend on the precise detection of landmarks inside facial pictures. Although landmark localization is a straightforward and intuitive operation for humans, for it to be replicated in machines, it has taken multiple years of research, giving rise to large number of high-quality image data sets, and an abrupt increase in the processing capacity of computers to attain near-human proficiency. While recognizing physical characteristics on a face, such as types of noses, is an intuitive and ordinary task for human vision, it has proven to be quite tough for computer vision, as it has not profited from millennia of evolution. Although facial images generally have similar content, common variations in pose, lighting, facial expression, and facial features can give rise to a multitude of problems for many computer vision systems, leading to substantial errors and mistakes in the task of computer vision face landmarking accuracy. In this study, we examine the two facial landmark identification techniques already in use-the Mediapipe Face Landmarker and Dlib's 68-point face landmark detection algorithm-to determine the conditions in which each method performs best.

Biometric features such as fingerprints, iris patterns, and face features help to identify people and restrict access to secure areas by performing advanced pattern analysis and matching. Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. However, the recognition results obtained by face recognition systems are a affected by several variations that may happen to the patterns in an unrestricted environment. As a result, several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition still remains as a difficult issue that needs to be resolved. In this paper, we propose a novel approach to tackling some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern (ELBP) description of all the local regions of the face. The relationship of each pixel with respect to its neighborhood is extracted and employed to calculate the new representation. ELBP reconstructs a much better textural feature extraction vector from an original gray level image in different lighting conditions. The dimensionality of the texture image is reduced by principal component analysis performed on each local face region. Each low dimensional vector representing a local region is now weighted based on the significance of the sub-region. The weight of each sub-region is determined by employing the local variance estimate of the respective region, which represents the significance of the region. The final facial textural feature vector is obtained by concatenating the reduced dimensional weight sets of all the modules (sub-regions) of the face image. Experiments conducted on various popular face databases show promising performance of the proposed algorithm in varying lighting, expression, and partial occlusion conditions. Four databases were used for testing the performance of the proposed system: Yale Face database, Extended Yale Face database B, Japanese Female Facial Expression database, and CMU AMP Facial Expression database. The experimental results in all four databases show the effectiveness of the proposed system. Also, the computation cost is lower because of the simplified calculation steps. Research work is progressing to investigate the effectiveness of the proposed face recognition method on pose-varying conditions as well. It is envisaged that a multilane approach of trained frameworks at different pose bins and an appropriate voting strategy would lead to a good recognition rate in such situation.

Introduction to Fingerprint Recognition, U. Halici, L.C. Jain, and A. Erol Fingerprint Feature Processing Techniques and Poroscopy, A.R. Roddy and J.D. Stosz Fingerprint Sub-Classification: A Neural Network Approach, G.A. Drets and H.G. Leljecstroem A Gabor Filter-Based Method for Fingerprint Identification, Y. Hamamoto Minutiae Extraction and Filtering from Gray-Scale Images, D. Maio and D. Maltoni Feature Selective Filtering for Ridge Extraction, A. Erol, U. Halici, and G. Ongun Introduction to Face Recognition, A.J. Howell Neural Networks for Face Recognition, A.S. Pandya and R.R. Szabo Face Unit Radial Basis Function Networks, A.J. Howell Face Recognition from Correspondence Maps, R.P. Wurtz Face Recognition by Elastic Bunch Graph Matching, L. Wiskott, J.-M. Fellous, N. Kruger, and C. von der Malsburg Facial Expression Synthesis Using Radial Basis Function Networks, I. King and X.Q. Li Recognition of Facial Expressions and Its Application to Human Computer Interaction, T. Onisawa and S. Kitazake

Facial recognition technologies are increasingly employed in security and law enforcement due to advances in computer vision and machine learning. Despite these advancements, balancing recognition accuracy, computational efficiency, and data security remains a significant challenge in research. This paper proposes a secure and efficient framework for criminal identification that integrates facial keypoint detection, normalization, Principal Component Analysis (PCA), and cryptographic hashing. The approach begins with grayscale conversion and image size standardization, followed by the detection of facial keypoints using pre-trained models to localize discriminative facial features. Normalization ensures scale and pose invariance, while PCA reduces feature dimensionality, improving computational performance without compromising representational fidelity. The reduced feature vectors are subsequently transformed into cryptographic hash values, providing unique, tamper-resistant identifiers for individuals. Experimental results demonstrate that the proposed method achieves high identification accuracy and computational efficiency while maintaining robustness against variations in lighting conditions and facial expressions. In addition, we introduce a hybrid deep feature–PCA fusion stage, where compact embeddings from a lightweight CNN are combined with normalized keypoints before being subjected to secure hashing. This fusion enhances robustness against occlusions and adversarial variations, providing a modern and secure update to traditional biometric pipelines.

This work presents a method to automatically locate facial feature points under large variations in pose, illumination and facial expressions. First we propose a method to calculate probabilistic-like output for each pixel of image. This probabilistic-like output describes the possibility of the pixel to be the center of specified object. A Gaussian mixture model is used to approximate the distribution of probabilistic-like output. The centers of these Gaussians are assigned with a probabilistic-like measure and they are considered as candidate feature points. There might be one or more candidate feature points in each facial region. A 3D model of facial feature points is built to enforce constraints on the localization results of feature points. Compared with active shape model (ASM) and its variant methods, our method could accommodate larger variations in pose, lighting and face expressions. Moreover, it is less sensitive to initialization errors, accurate, and fast. It takes a computer with P4 CPU about 10 ms to locate the five feature points (two eye centers, two mouth corners and nose tip). The feature localization accuracy is comparable with the accuracy of manually labeled features and it is robust to noise (glasses, beards). Experiments on FERET gallery and PIE are reported in this paper as well.

This research features a new method for automatic face recognition robust to variations in lighting, facial expression and eyewear. The new algorithm named SKKUfaces (Sungkyunkwan University faces) employs PCA (Principal Component Analysis) and FLD (Fisher's Linear Discriminant) in series similarly to Fisherfaces. The fundamental difference is that SKKUfaces effectively eliminates, in the reduced PCA subspace, portions of the subspace that are responsible for variations in lighting and facial expression and then applies FLD to the resulting subspace. This results in superb discriminating power for pattern classification and excellent recognition accuracy. We also propose an efficient method to compute the between-class scatter and within-class scatter matrices for the FLD analysis. We have evaluated the performance of SKKUfaces using YALE and SKKU facial databases. Experimental results show that the SKKUface method is computationally efficient and achieves much better recognition accuracy than the Fisherface method [1] especially for facial images with variations in lighting and eyewear.

2D Face recognition has been extensively studied for decades and has reached remarkable results in recent years. However, 2D Face recognition is sensitive to variations in poses, facial expressions and illuminations. Depth images provide valuable information to help model facial boundaries and understand the global facial layout and provide low frequency patterns. Intuitively, RGB-D images are more robust to external environments than RGB images. Unfortunately, RGB-D datasets are orders of magnitude smaller than 2D datasets and insufficient to train a deep CNN model as effective as RGB-based models. To tackle these challenges, we present an RGB-D ResNet50 model which can be transferred from a pretrained RGB model and takes RGB-D images as input. We achieved an accuracy of 94.64% and won the \(1^{\text {st}}\) place on 3D Face Recognition Algorithm Challenge, 2019 BenchCouncil International Artificial Intelligence System Challenges.

Face recognition with Symmetric Local Graph Structure (SLGS)