Abstract: A method for finding a temporal face sequence (412) includes, with a physical computing system (100), analyzing frames within a shot within a video, applying a face detection function to the frames, and in response to detecting a face within one of the frames, tracing a person associated with the face both backwards and forwards through frames within the shot. A temporal face sequence (412) is then defined as a sequence of frames that include frames within the shot spanning when the person is shown.

Abstract: A method for finding a temporal face sequence (412) includes, with a physical computing system (100), analyzing frames within a shot within a video, applying a face detection function to the frames, and in response to detecting a face within one of the frames, tracing a person associated with the face both backwards and forwards through frames within the shot. A temporal face sequence (412) is then defined as a sequence of frames that include frames within the shot spanning when the person is shown.

Abstract: A method and a system of live detection based on a physiological motion on a human face are provided. The method has the following steps: in step a, a motion area and at least one motion direction in visual angle of a system camera are detected and a detected facial region is found. In step b, whether a valid facial motion exists in the detected facial region is determined. If a valid facial motion is inexistent, the object is considered as a photo of human face, otherwise, the method proceeds to step c to determine whether the facial motion is a physiological motion. If not, the object is considered as the photo of human face, yet considered as a real human face. The real human face and the photo of human face can be distinguished by the present invention so as to increase the reliability of the face recognition system.

Abstract: In a method for character recognition based on Gabor filter group the Gabor filter's joint spatial/spatial-frequency localization and capability to efficiently extract characters' local structural features are employed to extract, from the character image, information of the stroke direction of characters as the recognition information of characters, so as to improve the capability to resist the noises, backgrounds, brightness variances in images and the deformation of characters. Using this information, a simple and effective parameter design method is put forward to optimally design the Gabor filter, ensuring a preferable recognition performance; a corrected Sigmoid function is used to non-linearly adaptively process the stroke direction information output from the Gabor filter group. When extracting the feature from blocks, Gaussian filter array is used to process the positive and negative values output from Gabor filter group to enhance the discrimination ability of the extracted features.