Human face classification using ultrasonic sonar imaging

In this paper, human face classification using ultrasonic sonar imaging is investigated. On the basis of Freedman's "image pulse" model, the scattering centers model is employed to simplify the complex geometry of the human face into a series of scattering centers. A chirp signal is utilized to detect the human face for its high range resolution and large signal-to-noise ratio. Ultrasonic sonar images, also named high-resolution range profiles, are obtained by demodulating the echoes with a reference chirp signal. Features directly related to the geometry of the human face are extracted from ultrasonic sonar images and verified in the experiments designed with different configurations of transmitter-receiver (TR) pairs. Experimental results indicate that the improved feature extraction method can achieve a high recognition rate of over 99% in the case of ultrasonic transmitters angled at 45° above and orthogonal to the face, and this method improves the performance of ultrasonic face recognition compared with our previous result.