Nonparametric Feature Matching Based Conditional Random Fields for Gesture Recognition from Multi-Modal Video

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 38; no. 8; pp. 1612 - 1625
• Main Author: Chang, Ju Yong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; conditional random field; Feature extraction; Gesture recognition; Gestures; Hand; Hidden Markov models; Humans; Indexes; nonparametric estimation; Pattern Recognition, Automated; structured learning; Support Vector Machine; Support vector machines; Three-dimensional displays; Training
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2016.2519021

We present a new gesture recognition method that is based on the conditional random field (CRF) model using multiple feature matching. Our approach solves the labeling problem, determining gesture categories and their temporal ranges at the same time. A generative probabilistic model is formalized and probability densities are nonparametrically estimated by matching input features with a training dataset. In addition to the conventional skeletal joint-based features, the appearance information near the active hand in an RGB image is exploited to capture the detailed motion of fingers. The estimated likelihood function is then used as the unary term for our CRF model. The smoothness term is also incorporated to enforce the temporal coherence of our solution. Frame-wise recognition results can then be obtained by applying an efficient dynamic programming technique. To estimate the parameters of the proposed CRF model, we incorporate the structured support vector machine (SSVM) framework that can perform efficient structured learning by using large-scale datasets. Experimental results demonstrate that our method provides effective gesture recognition results for challenging real gesture datasets. By scoring 0.8563 in the mean Jaccard index, our method has obtained the state-of-the-art results for the gesture recognition track of the 2014 ChaLearn Looking at People (LAP) Challenge.