Face Recognition Using Hidden Conditional Random Fields and Support Vector Machine
Huachun Yang · 2013
This paper proposes a face recognition method using hidden conditional random field (HCRF) model and support vector machine (SVM). Face image was looked on as composed of several parts from up to down. Face image was separated as a series of block in which histogram of oriented gradients (HOG) vector was extracted. SVM was used as a local discriminative model that outputs the association of the feature vectors with face parts. HCRF was used to model the dependencies between different parts. The method proposed in this paper achieves a higher recognition rate compared to the state-of-the-art in ORL database. The results indicate that integrating various dependencies between face parts plays an important role in face recognition.