GDL

Detection of person in the crowd is most exigent facial recognition aspect. Faces in a group can comprise the criticality of face localization, partial occlusion, and facial overlapping and real time background scenes. In this paper, single and multiple persons recognition is provided for group pictures. This offered method at first localized the facial area of the individuals. These identified faces are divided in full or the partial view clusters by observing the oval map. While performing the recognition, separate algorithms will be applied to both facial groups. Full face recognition is here implied using LBP, Gabor and structured feature fusion based distance analysis method. These methods will be applied individually and jointly to identify the facial image. Partial face recognition is here implied using discriminative structural analysis. This structural formation is here obtained using structure points and base curve identification. Maximum structural points and curve ratio map is considered as the identified image. Group images are captured at home, office, classroom and roadside with multiple situations. The experimentation interpretation is measured disjointedly for full view and partial view images obtained from complex scenes and scenarios. The evaluation is here applied to perform the facial region identification and the group photo recognition. The facial region extraction is applied on eight datasets. These datasets are IMM, Caltech, CMU, Bau, FDDB, LFW, Pointing and Self Collected Dataset. The facial extraction is applied on complex background images with single and multiple faces. The comparative evaluation shows that the localization stage of proposed model has reduced the FAR and FRR. The complete recognition model is applied on real time family dataset, random web celebrity dataset associated with LFW and FDDB datasets. The evaluation is provided for recognition of front and partial face images in different scenarios. The comparative results of facial recognition are evaluated against PCA, ICA and PCA-LDA methods. The results conclude that the proposed methods have improved the accuracy for both full and partial faces.

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