Learning of Graph Compressed Dictionaries for Sparse Representation Classification

Abstract

Abstract:Despite the limited target data available to design face models in video surveillance applications, many faces of non-target individuals may be captured in operational environments, and over multiple cameras, to improve robustness to variations. This paper focuses on Sparse Representation Classification (SRC) techniques that are suitable for the design of still-to-video FR systems based on under-sampled dictionaries. The limited reference data available during enrolment is complemented by an over-complete external dictionary that is formed with an abundance of faces from non-target individuals. In this paper, the Graph-Compressed Dictionary Learning (GCDL) technique is proposed to learn compact auxiliary dictionaries for SRC. GCDL is based on matrix factorization, and allows to maintain a high level of accuracy with compressed dictionaries because it exploits structural information to represent intra-class variations. Graph factorization compression has been shown to efficiently compress data, and can therefore rapidly construct compressed dictionaries. Accuracy and efficiency of the proposed technique is assessed and compared to reference sparse coding and dictionary learning technique using videos from the CAS-PEAL database. GCDL is shown to provide fast matching and adaptation of compressed dictionaries to new reference faces from the video surveillance environments.

An Extended Sparse Classification Framework for Domain Adaptation in Video Surveillance

“…Data can be presented as a collection of feature vectors or representation of the similarity/dissimilarity relations among data samples. Nourbakhsh et al [32] have proposed a graph compression method based on matrix factorization that focuses on structural information of the input data and the extension of the proposed method on DL is presented on [33]. Finally, several methods that combine DL and classification have recently been proposed like SVDL [34] and [35] to produce a compact auxiliary dictionary and classification in the same time.…”