Although automatic faces recognition has shown success for high-quality images under controlled conditions, for video-based recognition it is hard to attain similar levels of performance. We describe in this paper recent advances in a project being undertaken to trial and develop advanced surveillance systems for public safety. In this paper, we propose a local facial feature based framework for both still image and video-based face recognition. The evaluation is performed on a still image dataset LFW and a video sequence dataset MOBIO to compare 4 methods for operation on feature: feature averaging (Avg-Feature), Mutual Subspace Method (MSM), Manifold to Manifold Distance (MMS), and Affine Hull Method (AHM), and 4 methods for operation on distance on 3 different features. The experimental results show that Multi-region Histogram (MRH) feature is more discriminative for face recognition compared to Local Binary Patterns (LBP) and raw pixel intensity. Under the limitation on a small number of images available per person, feature averaging is more reliable than MSM, MMD, and AHM and is much faster. Thus, our proposed framework-averaging MRH feature is more suitable for CCTV surveillance systems with constraints on the number of images and the speed of processing.
In 2011, Chen et al. REF proposed a local facial feature framework for still image and video-based face recognition leveraging Feature Averaging (FA), Mutual Subspace Method (MSM), Manifold to Manifold Distance (MMD), and Affine Hull Method (AHM) resulting in high speed processing, especially for CCTV surveillance, but with the limitation of lower recognition rate, that is, only 90%.
Face recognition from still images to video sequences: a local-feature-based framework
EURASIP Journal on Image and Video Processing
Computer Science