Informatics Publishing Limited

S. Anila ¹, N. Devarajan ²

Affiliations
1 ECE Department, Sri Ramakrishna Institute of Technology, Coimbatore-10, IN
2 EEE Department, Government College of Technology, Coimbatore, IN

Abstract

Steganography (from Greek steganos, or "covered," and graphie, or "writing") is the hiding of a secret message within an ordinary message and the extraction of it at its destination. The new steganography algorithm which is constructed and implemented hides a large amount of data (image, audio, text) file into color BMP image. Adaptive image segmentation with bits replacement on the appropriate pixels has been used. These pixels are selected randomly rather than sequentially by using new concept defined by main cases with their sub cases for each byte in one pixel. Some specific image based steganography techniques show that an observer can indeed distinguish between images carrying a hidden message and images which do not carry a message. In this algorithm, 16 main cases with their sub cases cover all aspects of the input data into color bitmap image. High security layers have been proposed through three layers to make it difficult to break through the encryption of the input data.  Comparison is made with the previous steganography algorithms like S-Tools and factors like the amount of data that could be hidden, the nature of the image and the length of the password have also been discussed. The proposed algorithm can embed efficiently a large amount of data with high quality of the output.

Keywords

AES Algorithm, Decryption, Embed, Encryption, Hiding with High Security, Steganography.

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S. Anila ¹, N. Devarajan ²

Affiliations
1 Department of ECE, Sri Ramakrishna Institute of Technology, Coimbatore, IN
2 Government College of Technology, Coimbatore, IN

Abstract

Performance of the face verification system depends on many conditions. One of the most problematic is varying illumination condition. Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. This paper presents a simple and efficient preprocessing method that eliminates most of the effects of changing illumination and shadows while still preserving the essential appearance details that are needed for recognition. This preprocessing method run before feature extraction that incorporates a series of stages designed to counter the effects of illumination variations, local shadowing, and highlights while preserving the essential elements of visual appearance. Our method tackles this by combining the strengths of gamma correction, Difference Of Gaussian (DOG) filtering, contrast equalization. 1) Gamma correction enhances the local dynamic range of the image in dark or shadowed regions while compressing it in bright regions and at highlights. 2) DOG filtering that eliminates the shadowing effects while still preserving the essential appearance details that are needed for face recognition. 3) Contrast equalization rescales the image intensities to standardize a robust measure of overall contrast or intensity variations. The preprocessing method provides good performance on three sets that are widely used for testing under difficult lighting conditions:Extended Yale-B, Face Recognition Grand Challenge Version 2 experiment (FRGC-204), FERET datasets. The results obtained from the experiments showed that the illumination preprocessing methods significantly improves the recognition rate and it is a very important step in face verification system.

Keywords

Face Recognition, Gamma Correction, Illumination, Dog Filtering, Image Preprocessing, Contrast Equalization.

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