The human face is among the most significant objects in an image or video, it contains many important information and specifications, also is required to be the cause of almost all achievable look variants caused by changes in scale, location, orientation, pose, facial expression, lighting conditions and partial occlusions. It plays a key role in face recognition systems and many other face analysis applications.We focus on the feature based approach because it gave great results on detect the human face. Face feature detection techniques can be mainly divided into two kinds of approaches are Feature base and image base approach. Feature base approach tries to extract features and match it against the knowledge of the facial features.This paper gives the idea about challenging problems in the field of human face analysis and as such, as it has achieved a great attention over the last few years because of its many applications in various domains. Furthermore, several existing face detection approaches are analyzed and discussed and attempt to give the issues regarding key technologies of feature base methods, we had gone direct comparisons of the method's performance are made where possible and the advantages/ disadvantages of different approaches are discussed.

Ancient Iraq was the home of a major urban civilization which developed during 4000-3000 BCE. The Sumerians, who lived in Mesopotamia in southern Iraq, invented the cuneiform system of writing, which was an essential element of Sumerians culture. The translation of cuneiform is a highly complicated process. It is only in comparatively recent years that the grammar has been scientifically established, while the lexical problems are still numerous and far from resolved. Furthermore, most of the Sumerians tablets lost only few old images left, some of it saved in a special collection or worldwide museums. In this paper, we present a novel method used to obtain the cuneiform text from old Sumerian clay tablets, proposed method based on automatically select wavelet bases which it is essential and critical issues for wavelet algorithm implementation. Our procedure offers the archaeological and Cuneiformest an easy, fast and active method for extracting the cuneiform sentences. Experimental results of sample images show that the proposed system has superior result.

Head pose estimation is recently a more popular area of research. Challenging conditions, such as extreme pose, lighting, and occlusion, has historically hampered traditional, model-based methods. This paper presents a proposal of an integrated method for head pose estimation based on face detection and tracking. This method first locates certain facial features and based on their relative locations determine the head pose, the head pose estimated using coordinates of both eyes and a mouth relative to the nose as the origin of the coordinate system. The nose position is set up as the origin. The coordinates of the other parts defined from the origin, the distance between the face parts normalized so that the coordinates are independent of the image size. For facial feature detection from the detected face region, Haar-like feature utilized along with AdaBoost learning, the Adaboost learning algorithm used for creating optimized learning data. From the experiments, the proposed approach shows robustness in face and facial feature detection and eventually produces better results in estimating head pose rather than simply using Haar-like feature for both face and facial feature detection. The computational cost is low because it uses only those three points.

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Abstract