Soft-biometric detection based on supervised learning
Zhi Zhou, Glen Hong Ting Ong, Earn Khwang Teoh · 2014
In the past 5 years, people re-identification has been a popular topic as an application using computer vision techniques. Among the models used for people re-identification, soft-biometric traits based models have great potential due to the semantic meaning and robust performance they have. In this paper, we will exploit the performance of supervised learning based method on the detection of three soft-biometric traits: Glasses, Cap and Clothes Pattern. Simple features like edge and frequency are extracted from sample images and used for learning. Two supervised learning methods — Support Vector Machine (SVM) and Extreme Learning Machine (ELM) are employed and compared. Different normalization methods are compared as well. Experiments are carried out on images from FERET dataset and images collected online, and discussion is provided.