Gender recognition has gained more attention. However, most of the studies have focused on face images acquired under controlled conditions. In this paper, we investigate gender recognition on real-life faces. We prop...Gender recognition has gained more attention. However, most of the studies have focused on face images acquired under controlled conditions. In this paper, we investigate gender recognition on real-life faces. We proposed a gender recognition scheme, which is composed of four parts: face detection, median filter, feature extraction, and gender classifier. MULBP features are adopted and combined with a SVM classifier for gender recognition. The MULBP feature is robust to noise and illumination variations. In the experiment, we obtain 98.32% using LFW database and 97.30% on Samsung Gender dataset, which shows the superior performance in gender recognition compared with the conventional operators.展开更多
文摘Gender recognition has gained more attention. However, most of the studies have focused on face images acquired under controlled conditions. In this paper, we investigate gender recognition on real-life faces. We proposed a gender recognition scheme, which is composed of four parts: face detection, median filter, feature extraction, and gender classifier. MULBP features are adopted and combined with a SVM classifier for gender recognition. The MULBP feature is robust to noise and illumination variations. In the experiment, we obtain 98.32% using LFW database and 97.30% on Samsung Gender dataset, which shows the superior performance in gender recognition compared with the conventional operators.
