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Problem statement: Real-time secure image and video communication is challenging due to the processing time and computational requirement for encryption and decryption. In order to cope with these concerns, innovative image compression and encryption techniques are required. Approach: In this research, we have introduced partial encryption technique on compressed images and implemented the algorithm on Altera FLEX10K FPGA device that allows for efficient hardware implementation. The compression algorithm decomposes images into several different parts. We have used a secured encryption algorithm to encrypt only the crucial parts, which are considerably smaller than the original image, which result in significant reduction in processing time and computational requirement for encryption and decryption. The breadth-first traversal linear lossless quadtree decomposition method is used for the partial compression and RSA is used for the encryption. Results: Functional simulations were commenced to verify the functionality of the individual modules and the system on four different images. We have validated the advantage of the proposed approach through comparison, verification and analysis. The design has utilized 2928 units of LC with a system frequency of 13.42MHz. Conclusion: In this research, the FPGA prototyping of a partial encryption of compressed images using lossless quadtree compression and RSA encryption has been successfully implemented with minimum logic cells. It is found that the compression process is faster than the decompression process in linear quadtree approach. Moreover, the RSA simulations show that the encryption process is faster than the decryption process for all four images tested.

Artificial Neural Networks (ANN) are increasingly applied to biometric identification because neural nets have been shown to be technologically powerful and flexible, ideally suited to perform identification analysis. Therefore, it demands the development of a new processing structure that allows efficient hardware implementation of the neural networks mechanism. This research presents the ANN based iris recognition for biometric identification modeled by the very high speed integrated circuit Hardware Description Language (VHDL) to ease the description, verification, simulation and hardware realization of this kind of systems. The project is divided into two processes which are image processing and recognition. Image processing was performed by using Matlab where back propagation was used for recognition. The iris recognition architecture comprises of three layers: Input layer with three neurons, hidden layer with two neurons and output layer with one neuron. Sigmoid transfer function is used for both hidden layer and output layer neurons. Neuron of each layer is modeled individually using VHDL. Functional simulations were commenced to verify the functionality and performance of the individual modules and the system. Iris vector from captured human iris has been used to validate the effectiveness of the model. An accuracy of 88.6% is achieved in recognizing the sample of 100 data of irises.