Face Recognition Using Ensemble String Matching

• Published in: IEEE transactions on image processing Vol. 22; no. 12; pp. 4798 - 4808
• Main Authors: Chen, Weiping, Gao, Yongsheng
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2013; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Biometric Identification - methods; Construction; Cost function; Databases, Factual; Ensemble string matching; Exact sciences and technology; Face; Face - anatomy & histology; Face recognition; Female; Humans; Image processing; Image recognition; Information, signal and communications theory; Male; Matching; occlusion; partial matching; Pattern matching; Pattern recognition; Representations; Shape; Signal processing; sketch recognition; String matching; stringface; Strings; Studies; syntactic; Syntactics; Telecommunications and information theory; Transaction processing; Biometrics; Performance evaluation; Image recognition; Face recognition; Image processing; stringface; Pattern recognition; partial matching; Addressing; syntactic; occlusion; sketch recognition; Ensemble string matching; Feasibility; Automatic recognition
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2013.2277920

In this paper, we present a syntactic string matching approach to solve the frontal face recognition problem. String matching is a powerful partial matching technique, but is not suitable for frontal face recognition due to its requirement of globally sequential representation and the complex nature of human faces, containing discontinuous and non-sequential features. Here, we build a compact syntactic Stringface representation, which is an ensemble of strings. A novel ensemble string matching approach that can perform non-sequential string matching between two Stringfaces is proposed. It is invariant to the sequential order of strings and the direction of each string. The embedded partial matching mechanism enables our method to automatically use every piece of non-occluded region, regardless of shape, in the recognition process. The encouraging results demonstrate the feasibility and effectiveness of using syntactic methods for face recognition from a single exemplar image per person, breaking the barrier that prevents string matching techniques from being used for addressing complex image recognition problems. The proposed method not only achieved significantly better performance in recognizing partially occluded faces, but also showed its ability to perform direct matching between sketch faces and photo faces.