A Fractal based video compression method relies on the concept of self similarity within a frame and between successive frames in a video. There are two fractal compression methods called Cube based fractal compression and Frame based fractal compression. We have adopted the frame based video compression method, where the first frame is encoded using the intra frame compression technique and subsequent frames are compressed using inter frame compression technique. For, encoding each frame range and domain blocks are generated by fixed block partitioning scheme. Fractal codes are computed for every frame by comparing domain blocks from previous frame with range blocks of current frame and are stored for decoding purpose. In the decoding process, frames are recreated by loading stored fractal codes. Experimentation is performed by considering different sizes of range and domain blocks. Results are compared for different block sizes on the basis of compression ratio and PSNR (Peak Signal to Noise Ratio). It is observed that least sized range blocks provide higher PSNR at the cost of lower compression ratio.

Recognition of human facial expressions is an active research area that finds potential applications in areas such as human-computer interfaces, image retrieval and human emotion analysis. The face is the most extraordinary communicator, capable of accurately signaling emotion in a bore link of a second. Moreover facial expressions convey not only emotions, but other mental activities, social inter-action and physiological signals. In this paper we presented the evolution of the analysis of facial expressions and survey some of the pub-lished work in this area since 1990 to till date. This paper presents a time-line view of the advances made in this field, the facial parameteri-zation using FACS Action Units (AUs) and MPEG-4 Facial Animation Parameters (FAPs), the applications, and the Databases.