Text independent speaker gender recognition using lip movement

The conventional mouth gender recognition is based on a static image and ignore the dynamic information. In this paper, we propose a lip movement gender recognition method to improve the accuracy by exploring the dynamic information while a user is speaking. In order to overcome the difficulty caused by the nonlinear distribution of the lip images, Gausian Mixture Models (GMM) is adopted to represent the lip images. A similarity measure is defined to measure the difference between the successive frames. Gender recognition, as a soft biometric trait, can provide useful information for improving the performance of the speaker recognition systems. The accuracy of voice-based speaker gender recognition is high if the condition of the environment is good. But it will be drastically decreased if the test is conducted in a noisy environment. In this paper, we showed that lip movement, considered as a sequence of mouth images, can provide additional information than mouth alone for recognizing gender Experimental result obtained showed the effectiveness of the proposed method which is comparable to using just the voice information.