Face Recognition by Fusion of Local and Global Matching Scores using DS Theory: An Evaluation with Uni-classifier and Multi-classifier Paradigm

Faces are highly deformable objects which may easily change their appearance over time. Not all face areas are subject to the same variability. Therefore decoupling the information from independent areas of the face is of paramount importance to improve the robustness of any face recognition technique. This paper presents a robust face recognition technique based on the extraction and matching of SIFT features related to independent face areas. Both a global and local (as recognition from parts) matching strategy is proposed. The local strategy is based on matching individual salient facial SIFT features as connected to facial landmarks such as the eyes and the mouth. As for the global matching strategy, all SIFT features are combined together to form a single feature. In order to reduce the identification errors, the Dempster-Shafer decision theory is applied to fuse the two matching techniques. The proposed algorithms are evaluated with the ORL and the IITK face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition techniques also in the case of partially occluded faces or with missing information.Comment: 7 pages, 6 figures, IEEE Computer Vision and Pattern Recognition Workshop on Biometric

Activity-dependent bidirectional plasticity and homeostasis regulation governing structure formation in a model of layered visual memory

Poster presentation: Our work deals with the self-organization [1] of a memory structure that includes multiple hierarchical levels with massive recurrent communication within and between them. Such structure has to provide a representational basis for the relevant objects to be stored and recalled in a rapid and efficient way. Assuming that the object patterns consist of many spatially distributed local features, a problem of parts-based learning is posed. We speculate on the neural mechanisms governing the process of the structure formation and demonstrate their functionality on the task of human face recognition. The model we propose is based on two consecutive layers of distributed cortical modules, which in turn contain subunits receiving common afferents and bounded by common lateral inhibition (Figure 1). In the initial state, the connectivity between and within the layers is homogeneous, all types of synapses – bottom-up, lateral and top-down – being plastic. During the iterative learning, the lower layer of the system is exposed to the Gabor filter banks extracted from local points on the face images. Facing an unsupervised learning problem, the system is able to develop synaptic structure capturing local features and their relations on the lower level, as well as the global identity of the person at the higher level of processing, improving gradually its recognition performance with learning time. ..

Mapping Face Recognition Information Use across Cultures.

Face recognition is not rooted in a universal eye movement information-gathering strategy. Western observers favor a local facial feature sampling strategy, whereas Eastern observers prefer sampling face information from a global, central fixation strategy. Yet, the precise qualitative (the diagnostic) and quantitative (the amount) information underlying these cultural perceptual biases in face recognition remains undetermined. To this end, we monitored the eye movements of Western and Eastern observers during a face recognition task, with a novel gaze-contingent technique: the Expanding Spotlight. We used 2° Gaussian apertures centered on the observers' fixations expanding dynamically at a rate of 1° every 25 ms at each fixation - the longer the fixation duration, the larger the aperture size. Identity-specific face information was only displayed within the Gaussian aperture; outside the aperture, an average face template was displayed to facilitate saccade planning. Thus, the Expanding Spotlight simultaneously maps out the facial information span at each fixation location. Data obtained with the Expanding Spotlight technique confirmed that Westerners extract more information from the eye region, whereas Easterners extract more information from the nose region. Interestingly, this quantitative difference was paired with a qualitative disparity. Retinal filters based on spatial-frequency decomposition built from the fixations maps revealed that Westerners used local high-spatial-frequency information sampling, covering all the features critical for effective face recognition (the eyes and the mouth). In contrast, Easterners achieved a similar result by using global low-spatial-frequency information from those facial features. Our data show that the face system flexibly engages into local or global eye movement strategies across cultures, by relying on distinct facial information span and culturally tuned spatially filtered information. Overall, our findings challenge the view of a unique putative process for face recognition

Learning Local Features Using Boosted Trees for Face Recognition

Face recognition is fundamental to a number of significant applications that include but not limited to video surveillance and content based image retrieval. Some of the challenges which make this task difficult are variations in faces due to changes in pose, illumination and deformation. This dissertation proposes a face recognition system to overcome these difficulties. We propose methods for different stages of face recognition which will make the system more robust to these variations. We propose a novel method to perform skin segmentation which is fast and able to perform well under different illumination conditions. We also propose a method to transform face images from any given lighting condition to a reference lighting condition using color constancy. Finally we propose methods to extract local features and train classifiers using these features. We developed two algorithms using these local features, modular PCA (Principal Component Analysis) and boosted tree. We present experimental results which show local features improve recognition accuracy when compared to accuracy of methods which use global features. The boosted tree algorithm recursively learns a tree of strong classifiers by splitting the training data in to smaller sets. We apply this method to learn features on the intrapersonal and extra-personal feature space. Once trained each node of the boosted tree will be a strong classifier. We used this method with Gabor features to perform experiments on benchmark face databases. Results clearly show that the proposed method has better face recognition and verification accuracy than the traditional AdaBoost strong classifier

Swarm Optmization Algorithms for Face Recognition

In this thesis, a face recognition system based on swarm intelligence is developed. Swarm intelligence can be defined as the collective intelligence that emerges from a group of simple entities; these agents enter into interactions, sense and change their environment locally. A typical system for face recognition consists of three stages: feature extraction, feature selection and classification. Two approaches are explored. First, Bacterial Foraging Optimization(BFO), in which the features extracted from Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA) are optimized. Second, Particle Swarm Optimization(PSO), which optimizes the transform coefficients obtained from the Discrete Cosine Transform(DCT) of the images. PCA, LDA and DCT are all appearance-based methods of feature extraction. PCA and LDA are based on global appearance whereas DCT is performed on a block by block basis exploring the local appearance-based features. Finally, for classification Euclidean distance metric is used. The algorithms that have been applied are tested on Yale Face Database

การรู้จำภาพใบหน้าโดยใช้หลายคุณลักษณะด้วยการประมวลผลกราฟแสดงค่าความถี่ของระดับความเข้ม

Identification and authentication by face recognition mainly use global face features. However, the recognition performance is not good. This research aims to develop a method to increase the efficiency of recognition using global-face feature and local-face feature with 4 parts: the left-eye, right-eye, nose and mouth. This method is based on geometrical techniques used to find location of eyes, nose and mouth from the frontal face image. We used 110 face images for learning and testing. The histogram processed face recognition technique is used. The results show that the recognition percentage is 89.09%