Multiple Face Recognition Using Local Features and Swarm Intelligence

Chidambaram CHIDAMBARAM  Hugo VIEIRA NETO  Leyza Elmeri Baldo DORINI  Heitor Silvério LOPES  

IEICE TRANSACTIONS on Information and Systems   Vol.E97-D   No.6   pp.1614-1623
Publication Date: 2014/06/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E97.D.1614
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
local features,  iterative search,  face recognition,  

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Face recognition plays an important role in security applications, but in real-world conditions face images are typically subject to issues that compromise recognition performance, such as geometric transformations, occlusions and changes in illumination. Most face detection and recognition works to date deal with single face images using global features and supervised learning. Differently from that context, here we propose a multiple face recognition approach based on local features which does not rely on supervised learning. In order to deal with multiple face images under varying conditions, the extraction of invariant and discriminative local features is achieved by using the SURF (Speeded-Up Robust Features) approach, and the search for regions from which optimal features can be extracted is done by an improved ABC (Artificial Bee Colony) algorithm. Thresholds and parameters for SURF and improved ABC algorithms are determined experimentally. The approach was extensively assessed on 99 different still images - more than 400 trials were conducted using 20 target face images and still images under different acquisition conditions. Results show that our approach is promising for real-world face recognition applications concerning different acquisition conditions and transformations.