The face recognition is an efficient biometric system to identify a person. In this paper, we propose Face Recognition using Transform Domain Texture Features (FRTDTF). The face images are preprocessed and two sets of texture features are extracted. In first feature set, the Discrete Wavelet Transform (DWT) is applied on face image and considered only high frequency sub band coefficients to extract edge information efficiently. The Dual Tree Complex Wavelet Transform (DTCWT) is applied on high frequency sub bands of DWT to derive Low and High frequency DTCWT coefficients. The texture features of DTCWT coefficients are computed using Overlapping Local Binary Pattern (OLBP) to generate feature set 1. In second feature set, the DTCWT is applied on preprocessed face image and considered all frequency sub bands coefficients to extract significant information and edge information of face image. The texture features of DTCWT matrix are computed using OLBP to generate feature set 2. The final feature set is the concatenation of feature set 1 and set 2. The Euclidian distance (ED) is used to compare test image features with features of face images in the database. It is observed that, the performance parameter values are better in the case of proposed algorithm compared to existing algorithms.
The steganography is used for secure communication. In this paper we propose Dual Tree Complex Wavelet Transform (DTCWT) based high capacity steganography using coefficient replacement and adaptive scaling. The DTCWT is applied on cover image and Lifting Wavelet Transform2 (LWT2) is applied on payload to convert spatial domain into transform domain. The new concept of replacing HH sub band coefficients of DTCWT of cover image by LL sub band coefficients of payload is introduced to generate intermediate stego object. The adaptive scaling factor is used based on entropy of cover image to scale down intermediate stego object coefficient values to generate final stego object. It is observed that the capacity and security are increased in the proposed algorithm compared to existing algorithms.
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