Soft biometric classification using periocular region features
With periocular biometrics gaining attention recently, the goal of this paper is to investigate the effectiveness of local appearance features extracted from the periocular region images for soft biométrie classification. We extract gender and ethnicity information from the periocular region images using grayscale pixel intensities and periocular texture computed by Local Binary Patterns as our features and a SVM classifier. Results are presented on the visible spectrum periocular images obtained from the FRGC face dataset. For 4232 periocular images of 404 subjects, we obtain a baseline gender and ethnicity classification accuracy of 93% and 91%, respectively, using 5-fold cross validation. Furthermore, we show that fusion of the soft biométrie information obtained from our classification approach with the texture based periocular recognition approach results in an overall performance improvement.