Biometrics recognition aims to identify and predict new personal identities based on their existing knowledge. As the use
of multiple biometric traits of the individual may enables more information to be used for recognition, it has been proved
that multi-biometrics can produce higher accuracy than single biometrics. However, a common problem with traditional
machine learning is that the training and test data should be in the same feature space, and have the same underlying
distribution. If the distributions and features are different between training and future data, the model performance often
drops. In this paper, we propose a transfer learning method for face recognition on bimodal biometrics. The training and
test samples of bimodal biometric images are composed of the visible light face images and the infrared face images. Our
algorithm transfers the knowledge across feature spaces, relaxing the assumption of same feature space as well as same
underlying distribution by automatically learning a mapping between two different but somewhat similar face images.
According to the experiments in the face images, the results show that the accuracy of face recognition has been greatly
improved by the proposed method compared with the other previous methods. It demonstrates the effectiveness and
robustness of our method.
Dim small target detection, which is characterized by complex background and low Signal-to-Noise Ratio (SNR), is critical
for many applications. Traditional detection algorithms assume that noise is not useful for detecting targets and try to
remove the noise to improve SNR of images using various filtering techniques. In this paper, we introduce a detection
algorithm based on Stochastic Resonance (SR) where stochastic resonance is used to enhance the dim small targets. Our
intuition is that SR can achieve the target enhancement in the presence of noise. Adaptive Least Mean Square (ALMS)
filtering is first adopted to estimate the background, and the clutter is suppressed by subtracting the estimated background
image from the source image. Adaptive SR (ASR) method is then employed to enhance the target and improve the SNR
of the image containing the target and noise. ASR tunes and adds the optimal noise intensity to increase the power of the
targets and therefore improve the SNR of the image. Several experiments on synthetic and natural images are conducted to
evaluate our proposed algorithm. The results demonstrate the effectiveness of our algorithm.