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.
Based on mean preserving bi-histogram equalization (BBHE), an adaptive image histogram equalization algorithm for
contrast enhancement is proposed. The threshold is gotten with adaptive iterative steps and used to divide the original
image into two sub-images. The proposed Iterative of Brightness Bi-Histogram Equalization overcomes the
over-enhancement phenomenon in the conventional histogram equalization. The simulation results show that the
algorithm can not only preserve the mean brightness, but also keep the enhancement image information effectively from
visual perception, and get a better edge detection result.
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