Handwritten Signatures are special types of behavioural biometric which are used in many applications such as banks, credit cards, passport, check processing, and financial documentation, etc. Verification of these signatures is a challenging task especially in the case of offline where there is no information of signing process. So there is a need for a system that can distinguish between the genuine and the forged signature to avoid the chances of theft or fraud. Many types of researches have been done in this area in the last three decades. Earlier this task was performed by handcrafted features and recently deep learning techniques have been employed for this task, but still, there is a chance of enhancement in the accuracy of the system. In this paper, we present a comprehensive study of the work done in the field of offline signature verification and also the challenges which are still present in this area.

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Abstract-Wrinkles plays an imperative part in the face-based investigation. They have been broadly utilized as a part of uses, for example, facial correcting, outward appearance acknowledgment, and face age estimation. In spite of the fact that a couple of strategies for a wrinkle investigation have been investigated in the writing, poor recognition confines the precision and dependability of wrinkle division. Wrinkles display 3D type of skin and show up as dexterous discontinuities or splits in encompassing skin surface. There are diverse strategies show for facial wrinkles discovery.