Face recognition using part-based dense sampling local features

For years, researchers have made great efforts to find an appropriate face representation for face recognition. A fusion strategy of Local Binary Pattern (LBP) and Gabor filters yields great achievements. LBP is good at coding fine details of facial appearance and texture, whereas Gabor features can encode facial shape and appearance over a range of coarser scales. Despite the great performance, this fusion representation suffers from low effectiveness and resolution variance. In this paper, we propose a novel representation strategy of face images which is fast and robust to resolution variance. We apply dense sampling around each detected feature point, extract Local Difference Feature (LDF) for face representation, then utilize Principal Component Analysis (PCA)+Linear Discriminant Analysis (LDA) to reduce feature dimension and finally use cosine similarity evaluation for recognition. We have utilized our proposed face representation strategy on two databases, namely self-collected Second Generation ID Card of China and Driver׳s License (SGIDCDL) database and public Facial Recognition Technology (FERET) database. Our experimental results show that the proposed strategy has good performance on face recognition with fast speed.