Abstract-This paper presents a DWT-DCT-SVD based hybrid watermarking method for color images. Robustness is achieved by applying DCT to specific wavelet sub-bands and then factorizing each quadrant of frequency sub-band using singular value decomposition. Watermark is embedded in host image by modifying singular values of host image. Performance of this technique is then compared by replacing DCT by Walsh in above combination. Walsh results in computationally faster method and acceptable performance. Imperceptibility of method is tested by embedding watermark in HL2, HH2 and HH1 frequency subbands. Embedding watermark in HH1 proves to be more robust and imperceptible than using HL2 and HH2 sub-bands.

Iris recognition has been a fast growing, challenging and interesting area in real-time applications. A large number of iris recognition algorithms have been developed for decades. The paper presents novel Haarlet Pyramid based iris recognition technique. Here iris recognition is done using the image feature set extracted from Haar Wavelets at various levels of decomposition. Analysis was performed of the proposed method, consisting of the False Acceptance Rate and the Genuine Acceptance Rate. The proposed technique is tested on an iris image database having 384 images. The results show that Haarlets level-5 outperforms other Haarlets, because the higher level Haarlets are giving very fine texture features while the lower level Haarlets are representing very coarse texture features which are less useful for discrimination of images in iris recognition.

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The paper discusses novel image retrieval methods based on edge texture of images extracted using morphological operators. The existing CBIR techniques are based on the feature vectors extracted from morphological edge extraction techniques such as simple morphological edge extraction technique, Top-Hat transform and Bottom-Hat transform. The proposed CBIR techniques are using the morphological edge extraction techniques with block truncation coding (BTC). The proposed techniques are tested on generic image database with 1000 images spread across 11 categories. In all 55 queries (5 from each category) are fired on the image database. The average precision and recall of all queries are computed and considered for performance analysis. The experimental results show that use of BTC over morphological shape images for feature extraction improves the performance of image retrieval with reduced computational complexity for query execution. In all BTC with simple morphological edge extraction based CBIR method (SMBTC) gives best performance.

Face recognition has been a fast growing, challenging and interesting area in real-time applications. A large number of face recognition algorithms have been developed for decades. The paper presents novel Haatlet Pyramid based face recognition technique. Here face recognition is done using the image feature set extracted from Haarlets applied on the image at various levels of decomposition. Here the image features are extracted by applying Haarlets on gray plane (average of red, green and blue. The proposed technique is tested on two image databases having 100 images each. The results show that Haarlets level-3 and Haarlets level-4 outperforms other Haarlets, because the higher level Haarlets are giving very coarse texture features while the lower level Haarlets are representing very fine texture features which are less useful to differentiate images in face recognition..