Eigenface

About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.

RedFace: um sistema de reconhecimento facial baseado em técnicas de análise de componentes principais e autofaces: comparação com diferentes classificadores

Abstract: Developing a computational model for facial recognition is not an easy task, because faces and multi-dimensional visual stimulation have complex modeling features. The difficulty lies in a face modeling which set aside the features that differ from other faces, since they have a few substantial differences among themselves. Although different, all faces have features such as: a mouth, two eyes and a nose. In this work, we propose a facial recognition system developed in two stages. First, were used techniques of Principal Components Analysis (PCA) and Eigenfaces in order to extract the face features. Then, the classifiers K-Nearest Neighbors (K-NN), Random Forest and K-Star will be applied in the face recognition process. The algorithms validation was held in a database with 1280 images of 64 different classes. Finally, was evidenced that the performance tests of the algorithms in face recognition systems based on PCA were consider very satisfactory, reaching the best recognition rates above 90% in all classifiers. Finding out the best technique, it will be applied in the students identification who access a Virtual Learning Environment.

Intuitive quasi-eigen faces

Abstract: In blendshape-based facial animation, two main approaches are used to create the key expressions: manual sculpting and statistically-based techniques. Hand-generated expressions have the advantage of being intuitively recognizable, thus allowing animators to use conventional keyframe control. However, they may cover only a fraction of the expression space, resulting in large reproduction/animation errors. On the other hand, statistically-based techniques produce eigenfaces that give minimal reproduction errors but are visually non-intuitive. In this paper we propose a technique to convert a given set of hand-generated key expressions into another set of so-called quasi-eigen faces. The resulting expressions resemble the original hand-generated expressions, but have expression space coverages more like those of statistically generated expression bases. The effectiveness of the proposed technique is demonstrated by applying it to hand-generated expressions.

Analysis of the effect of using non-composite multi-channel raw color images on face recognition accuracy with arbitrary large off-the-plane rotations

Abstract: The need for a solution capable of identifying individuals from a distance, possibly wearing a disguise and in a crowded and busy environment is steadily increasing in many homeland security applications. The use of face recognition techniques has unique advantages when compared to other biometric methodologies - face images can be obtained from a distance and without the cooperation of the subject. A recent example demonstrating the growing need for such solution are the videos released by some terrorist organizations where the suspect's face is occluded with only the eyes being visible. Research has yielded several state-of-the-art algorithms but their accuracy is greatly lowered in the presence of various factors. Face recogition with arbitrary large face off-the-plane rotation remains one very challenging and open research problem. In order to build better face recognition algorithms, it is important to identify and analyze what and how factors affect the accuracy of said algorithms so these methods can be improved and made more robust. In our previous work we presented how factors such as image registration, number and type of training templates and the presence of varying amount of partial face information that can be used to improve the performance of three very popular widely used face recognition algorithms. In this paper, we examine how using multi-channel color images instead of their one-channel grayscale/color composite improves face recognition accuracy in the challenging case when face arbitrary large off-the-plane rotation is present. We demonstrate the improvement to face recognition accuracy through the very popular and widely used Eigenface-based, Fisherface-based and Direct Correlation-based algorithms. Our findings and experimental results with the data, show that when using frontal-facing images as external test images with frontal-facing images as training set images, the additional information contained in the multi-channel color images does not improve the composite gray-scale face recogntition accuracy. However, in the case where the algorithms are trained using only images with slight or considerable off-the-plane rotation, and externally tested either on frontal-facing images or images with arbitrary off-the-plane rotation, the information provided by the multi-channel color images boosts the face recognition accuracy for all three recognition algorithms.

A DCT-based adaptive metric learning model using asymptotic local information measure

Abstract: We present an adaptive metric learning vector quantization procedure based on the discrete-cosine transform (DCT) for accurate face recognition used in multimedia applications. Since the set of learning samples may be small, we employ a mixture model of prior distributions. The model selection method, which minimizes the cross entropy between the real distribution and the modeled one, is presented to optimize the mixture number and local metric parameters. The structural risk minimization is used to facilitate an asymptotic approximation of the cross entropy for models of fixed complexity. We also provide a formula to estimate the model complexity derived from the minimum description length criterion. The structural risk minimization method proposed achieves an recognition error rate of 2.29% using the ORL database, which is better than previously reported numbers using the Karhunen-Loeve transform convolution network, the hidden Markov model and the eigenface model.

New multi-biometric approaches for improved person identification

Abstract: Multiple modality biometric approaches are proposed integrating two-dimensional face appearance with ear appearance, three-dimensional face shape, and the pattern of heat emission on face A single source biometric recognition method, such as face, has been shown to improve its identification rate by incorporating other biometric sources The investigation of multi-modal biometrics involves a variety of sensors For the recognition task, each sensor captures different aspects of human facial features; for example, appearance representing the levels of brightness on surface reflectance by a light source, shape data representing depth values defined at points on an object, and the pattern of heat emitted from an object The results of our multiple biometric approach shown in this investigation appear to support the conclusion that the path to higher accuracy and robustness in biometrics involves the use of multiple biometrics rather than the best possible sensor and algorithm for a single biometric A new evaluation scheme is designed to assess the improvement gained by multiple biometrics Because multi-modal recognition employs multiple samples of facial data, it is also possible that the improvement achieved over considering multiple samples from all modalities for recognition Therefore, this evaluation scheme will determine the recognition accuracy gained by multiple modality approach and multiple sample approach Also, a new algorithm for 3D face recognition is proposed for handling expression variation It uses a surface registration-based technique for 3D face recognition We evaluate and compare the performance of approaches to 3D face recognition based on PCA-based and on iterative closest point algorithms The proposed 3D face recognition method is fully automatic to use to initialize the 3D matching The evaluation results show that the proposed algorithm substantially improves performance in the case of varying facial expression This is the first study to compare the PCA and ICP approaches to 3D face recognition, and the first to propose a multiple-region approach to coping with expression variation in 3D face recognition The proposed method outperforms 3D eigenfaces when 3D face scans were acquired in different times without expression changes and also with expression changes