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Personal identification based on fingerprints is very popular compared to identification based on other biometric features like iris, gait and face etc. Performance of automatic fingerprints identification systems depends upon the quality of fingerprints. Extraction of fingerprints features in poor quality fingerprints is challenging task. Accurate measurement of fingerprints feature improves the accuracy of identification systems. Fingerprints consists of ridge and valley structures and offers different types of features, they are categorized as level 1, level 2 and level 3 features. Level 1 features are singular points which are used for fingerprints registration, classification etc. Level 2 features are ridge features like minutiae points, ridge orientation etc., and commercially available fingerprint recognition systems are based on level 2 features. Level 3 features include sweat pores, incipient ridges etc. Among these features ridge orientation is used for fingerprint enhancement, fingerprint classification, indexing and fingerprint segmentation. This paper provides an overview of existing state of the art techniques for ridge orientation estimation.

Object detection is a computerized technology related to image processing used in images and videos, to identify instances of real world object. Object detection is usually achieved by edge detection. Edge detection is a process to identify the edges of image by finding the place where intensity changes rapidly. In this experiment an attempt is made to detect the objects by using canny edge detection method in java. The detected objects can be further used for watermarking purpose. The comparison between canny and sobel edge detection is carried out. Morphological filters are used to improve canny edge detection. Contour based learning technique is used to draw contours for detected objects.