Leonardo A. Cament

Methodological improvement on local Gabor face recognition based on feature selection and enhanced Borda count

Face recognition has a wide range of possible applications in surveillance, human computer interfaces and marketing and advertising goods for selected customers according to age and gender. Because of the high classification rate and reduced computational time, one of the best methods for face recognition is based on Gabor jet feature extraction and Borda count classification. In this paper, we propose methodological improvements to increase face recognition rate by selection of Gabor jets using entropy and genetic algorithms. This selection of jets additionally allows faster processing for real-time face recognition. We also propose improvements in the Borda count classification through a weighted Borda count and a threshold to eliminate low score jets from the voting process to increase the face recognition rate. Combinations of Gabor jet selection and Borda count improvements are also proposed. We compare our results with those published in the literature to date and find significant improvements. Our best results on the FERET database are 99.8%, 99.5%, 89.2% and 86.8% recognition rates on the subsets Fb, Fc, Dup1 and Dup2, respectively. Compared to the best results published in the literature, the total number of recognition errors decreased from 163 to 112 (31%). We also tested the proposed method under illumination changes, occlusions with sunglasses and scarves and for small pose variations. Results on two different face databases (AR and Extended Yale B) with significant illumination changes showed over 90% recognition rate. The combination EJS-BTH-BIP reached 98% and 99% recognition rate in images with sunglasses and scarves from the AR database, respectively. The proposed method reached 93.5% recognition on faces with small pose variation of 25? rotation and 98.5% with 15% rotation in the FERET database.

Fusion of local normalization and Gabor entropy weighted features for face identification

Face recognition is one of the most extensively studied topics in image analysis because of its wide range of possible applications such as in surveillance, access control, content-based video search, human-computer interaction, electronic advertisement and more. Face identification is a one-to-n matching problem where a captured face is compared to n samples in a database. In this work we propose two new methods for face identification. The first one combines entropy-like weighted Gabor features with the local normalization of Gabor features. The second fuses the entropy-like weighted Gabor features at the score level with the local binary pattern (LBP) applied to the magnitude (LGBP) and phase (LGXP) components of the Gabor features. We used the FERET, AR, and FRGC 2.0 databases to test and compare our results with those previously published. Results on these databases show significant improvement relative to previously published results, reaching the best performance on the FERET and AR databases. Our methods also showed significant robustness to slight pose variations. We tested the proposed methods assuming noisy eye detection to check their robustness to inexact face alignment. Results show that the proposed methods are robust to errors of up to 3 pixels in eye detection. HighlightsWe propose an entropy weighted strategy over selected Gabor features.We fuse local normalization with Gabor features to improve face identification.Fusion of different features improve face identification relative to cases with no fusion.Our results are compared advantageously on several international face databases.We tested our methods with variable illumination, gesticulation, pose and occlusion.

Face recognition under pose variation with local Gabor features enhanced by Active Shape and Statistical Models

Face recognition is one of the most active areas of research in computer vision. Gabor features have been used widely in face identification because of their good results and robustness. However, the results of face identification strongly depend on how different are the test and gallery images, as is the case in varying face pose. In this paper, a new Gabor-based method is proposed which modifies the grid from which the Gabor features are extracted using a mesh to model face deformations produced by varying pose. Also, a statistical model of the scores computed by using the Gabor features is used to improve recognition performance across pose. Our method incorporates blocks for illumination compensation by a Local Normalization method, and entropy weighted Gabor features to emphasize those features that improve proper identification. The method was tested on the FERET and CMU-PIE databases. Our literature review focused on articles with face identification with wide pose variation. Our results, compared to those of the literature review, achieved the highest classification accuracy on the FERET database with 2D face recognition methods. The performance obtained in the CMU-PIE database is among those obtained by the best methods published. HighlightsA local matching Gabor method is improved with an Active Shape and a Statistical Model.The enhanced model is applied to recognize faces with significant pose variation.Comprehensive tests were performed on the FERET and CMU-PIE databases.Results improved from 31.1% to 70.57% in the extreme poses of the databases.We reached the highest results with pose variation compared to any previous 2D method.

Local matching Gabor entropy weighted face recognition

Face recognition has a wide range of possible applications in surveillance, access control, human computer interfaces and in electronic marketing and advertising for selected customers. Several models based on Gabor feature extraction have been proposed for face recognition with very good results on internationally available face databases. In this paper, we propose a methodological improvement to increase face recognition rate by selection and weighting Gabor jets by an entropy measure. We also propose improvements in the Borda count classification through a threshold to eliminate low score jets from the voting process to increase the face recognition rate. We show that combinations of weighting Gabor jets and threshold Borda yield the best results. We tested our methodological improvements on the FERET and the AR face databases. On the FERET database we reduce the total number of errors from 163 to 102 which is the highest score published up to date. The total number of errors in face recognition was reduced in 37%. On the AR database we also obtained important improvements and tested face images with illumination and gesticulation changes, and occlusions.

Active Clustering with Ensembles for Social structure extraction

We introduce a method for extracting the social network structure for the persons appearing in a set of video clips. Individuals are unknown, and are not matched against known enrollments. An identity cluster representing an individual is formed by grouping similar-appearing faces from different videos. Each identity cluster is represented by a node in the social network. Two nodes are linked if the faces from their clusters appeared together in one or more video frames. Our approach incorporates a novel active clustering technique to create more accurate identity clusters based on feedback from the user about ambiguously matched faces. The final output consists of one or more network structures that represent the social group(s), and a list of persons who potentially connect multiple social groups. Our results demonstrate the efficacy of the proposed clustering algorithm and network analysis techniques.

Illumination compensation method for local matching Gabor face classifier

Illumination compensation has proven to be crucial in face detection and face recognition. Several methods for illumination compensation have been developed and tested on the face recognition task using international available face databases. Among the methods with best results are the Discrete Cosine Transform (DCT), Local Normalization (LN) and Self-Quotient Image (SQI). Most of these methods have been applied with great success in face recognition using a principal component classifier (PCA). In the past few years, Local Matching Gabor (LMG) classifiers have shown great success in face classification relative to other classifiers. In this work we optimize several illumination compensation methods using the LMG face classifier. We use ge netic algorithms as the optimization tool. We test our results on the FERET international face database. Results show that face recognition can be significantly improved by illumination compensation methods. The best results are obtained with the optimized LN method which yields a 31% reduction in the total number of errors in the FERET database.

Face recognition under pose variation with active shape model to adjust Gabor filter kernels and to correct feature extraction location

Gabor features have been used widely in face identification because of their good results and robustness. However, face identification is strongly affected when the test images are very different from those of the gallery, as is the case in varying face pose. In this paper, a new 2D Gabor-based method is proposed that modifies the grid from which the Gabor features are extracted using a mesh to model face deformations produced by varying pose. Also the Gabor filter kernels are modified by the deformation field computed using the active shape model adjusted to the face. Therefore, the position of the Gabor jets with respect to the face features (eyes, nose, mouth, etc.) is closer to the original position in the frontal face and the modified Gabor filter kernel adjusts to the varying pose. Our method incorporates blocks for illumination compensation by a Local Normalization method, and entropy-weighted Gabor features to emphasize those features that yield proper identification. The method was assessed on the FERET database including pose variations for ±60°, ±40°, ±25°, ±15°, and 0°. We compared our results to those previously published for 2D methods. Our proposed method achieved the highest classification accuracy on the FERET database.