598,834 research outputs found
Maximized Posteriori Attributes Selection from Facial Salient Landmarks for Face Recognition
This paper presents a robust and dynamic face recognition technique based on
the extraction and matching of devised probabilistic graphs drawn on SIFT
features related to independent face areas. The face matching strategy is based
on matching individual salient facial graph characterized by SIFT features as
connected to facial landmarks such as the eyes and the mouth. In order to
reduce the face matching errors, the Dempster-Shafer decision theory is applied
to fuse the individual matching scores obtained from each pair of salient
facial features. The proposed algorithm is evaluated with the ORL and the IITK
face databases. The experimental results demonstrate the effectiveness and
potential of the proposed face recognition technique also in case of partially
occluded faces.Comment: 8 pages, 2 figure
Robust Textural Features for Real Time Face Recognition
Automatic face recognition in real life environment is challenged by various issues such as the object motion, lighting conditions, poses and expressions. In this paper, we present the development of a system based on a refined Enhanced Local Binary Pattern (ELBP) feature set and a Support Vector Machine (SVM) classifier to perform face recognition in a real life environment. Instead of counting the number of 1\u27s in ELBP, we use the 8-bit code of the thresholded data as per the ELBP rule, and then binarize the image with a predefined threshold value, removing the small connections on the binarized image.
The proposed system is currently trained with several people\u27s face images obtained from video sequences captured by a surveillance camera. One test set contains the disjoint images of the trained people\u27s faces to test the accuracy and the second test set contains the images of non-trained people\u27s faces to test the percentage of the false positives. The recognition rate among 570 images of 9 trained faces is around 94%, and the false positive rate with 2600 images of 34 non-trained faces is around 1%. Research work is progressing for the recognition of partially occluded faces as well. An appropriate weighting strategy will be applied to the different parts of the face area to achieve a better performance
Understanding critical factors in gender recognition
Gender classification is a task of paramount importance in face recognition research, and it is potentially useful in a large set of applications. In this paper we investigate the gender classification problem by an extended empirical analysis on the Face Recognition Grand Challenge version 2.0 dataset (FRGC2.0). We propose challenging experimental protocols over the dimensions of FRGC2.0 – i.e., subject, face expression, race, controlled or uncontrolled environment. We evaluate our protocols with respect to several classification algorithms, and processing different types of features, like Gabor and LBP. Our results show that
gender classification is independent from factors like the race of the subject, face expressions, and variations of controlled illumination conditions. We also report that Gabor features seem to be more robust than LBPs in the case of uncontrolled environment
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