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Video-based Heart Rate Measurement has drawn a lot of attention in recent years. A few papers have been proposed in this work and most of them applied Fourier transform peak interval determination method for heart rate measurement. However, the analyzing duration for one outcome in these methods is usually longer than 15 seconds. This is because that the measurement resolution in Fourier transform is proportional to the number of samples. In this paper, we apply a novel method to combine temporal-domain peak determination method and super resolution method for heart rate measurement. And we further propose a spectrum selection scheme and a data shift scheme to raise the measurement accuracy. The video database taken in our lab is used to evaluate the performance of the proposed method. The experimental results show two important things. The first is that when the analyzing duration of the proposed method is one third of Fourier transform, the precision of the proposed method is only a little lower than that of the Fourier transform method. Furthermore the precision of the proposed method is superior to Fourier transform approach when the analyzing duration is the same for both methods.

Spoofing attack (or copy attack) is a fatal threat for biometric authentication systems. In this paper, we present a novel liveness detection method based on frequency entropy of image sequences against the photograph spoofing. By splitting the color video of face region into RGB channels, we can obtain the time sequences of each color channel. Then, three RGB sequences are analyzed through Independent Component Analysis (ICA) algorithm to eliminate the cross-channel image noises. Moreover, the Fast Fourier Transform (FFT) is further applied to these signals to get the power spectra of each RGB channels. Finally, the power spectra are verified through the entropy calculation to validate the liveness or photo attack. The experimental results demonstrate the superiority of the proposed method, which has an accuracy of more than 95%.

In previous studies, the signal subspace technique for speech enhancement was extended and a perceptually constrained generalized singular value decomposition (PCGSVD)-based algorithm [1] was developed which properly integrated the auditory masking effect and the GSVD algorithm. Both objective measures and subjective tests verified that this approach can offer better performance than the GSVD-based approach and the conventional spectral subtraction (SS) algorithm. But very high computational complexity is required in the PCGSVD-based method when performing the matrices decomposition via the GSVD algoruthm. In this paper, we properly utilize the time-shift property of DFT and the special structure of Hankel matrices to perform similar functions previously offered by GSVD, and a perceptually constrained minimum variance estimation algorithm is developed. By replacing GSVD algorithm with DFT, the computation complexity is significantly reduced, almost the same as the conventional SS algorithm. Experiments showed that comparable performance to that of the PCGSVD-based approach can be achieved, regardless of whether the additive noise is stationary or not, specially when it is non-white.

The present invention provides a method for eliminating a musical tone from becoming a wind shear sound, which is used for the music recording file played in the GSM mobile communication system, such as the personal ring back tone. The method is to judge a portion of the music recording file where the music tone is erroneously judged as a background music, and add a small section of silence in front of the portion, so that the GSM mobile communication system would not judge the portion of the music tone as the background music so as to effectively eliminate the generation of the wind shear sound.