For criminal investigators in the field of forensic science the face recognition is challenging task. As the forensic face images captured under non-ideal conditions with complex background and variation in the pose. This paper is implemented PCA and Eigenface method for face recognition with variation in the pose. The purpose of PCA is to reduce the large dimensionality of the facial data space to the smaller intrinsic dimensionality of feature space. The face images are projected onto face space simply by multiplying the difference between the image and the average and the result is multiplied by each eigenvector. The result of this operation will be the weighted contribution of each Eigenface in representing the input face image, treating the Eigenfaces as a basis set for face images. The Euclidean distance taken from the features of captured face image and available facial features will determine the matching of the face image. The Principal Component Analysis (PCA) using Eigenface method is an efficient method for face recognition in the field of forensic science. The experimental results shows that the proposed method is best suited for face recognition with variation in the pose.

Video-surveillance systems are one of the main source of information during investigations. However, the adopted closed-circuit devices are often affected by poor quality mainly because of economical and practical problems. In many cases a low quality image can give useful information during the first phase of the investigation. As a consequence, the images and sequences coming from video-surveillance systems need to be digitalized in order to be processed to enhance and extract features useful for crime analysis. Visual information is the most appealing and intuitive mode of conveying information. Further, the amount of information that video carries is significantly greater than that carried by any other media. From surveillance for security, to capturing and analyzing medical procedures, to forensic applications. The greatest users of video information constitute the surveillance cameras used for security. The content in such applications constitutes hours of video usually taken from a single camera that is stationary. For such applications, it is usually necessary to trace certain anomalous events or individuals .In particular, airport surveillance cameras, surveillance camera in traffic, shopping malls are very good examples. The purpose of these cameras is to detect suspicious activity, like a perpetrator leaving a piece of luggage that might be harmful, or picking up something that might not belong to him or her, Firing from a vehicle, suspicious activity in shopping malls etc. In this work, the task is the automatic segmentation of continuous video into activity-based segments. It is different from the conventional shot-detection and scene change problems of video processing. The information being searched for is not the boundary between two different video contents in a single video, but a significant change in the activity within a single scene in the video. Thus, a temporal segmentation of the video into different events is desired. The approach used is to combine some of the conventional shot-detection methods with some new techniques for video-content analysis .

Digital images provide a new way to represent pictures and scenes that only film and a darkroom could supply before. This new way to capture and store images opens a door to malicious individuals wishing to forge or otherwise manipulate original authentic images. Since digital photography is improving and becoming more widely used technology, a need exists to provide countermeasures against malicious forgers. The old adage “don’t believe everything you hear” is becoming “don’t believe everything you see.” Photo is considered as evidence in all digital era. Image Forensic area provide right value to authenticated original images. We proposed two algorithm to overcome such challenging problem in the forensic world. Much time and effort has gone into analyzing uncompressed images but current techniques return dismal success in detecting one of the most common digital image formats, JPEG. In this paper we presented here attempts to tailor methods toward the JPEG format as well as incorporate all image formats where possible. The proposed method is analogous to a recursive type process, with the sub-processing resembling a “divide and conquer” approach. Block Based Processing is useful because the calculations performed are influenced by only the information present in that particular block. A JPEG image can either be color or grayscale. For grayscale Images we propose direction filter technique and color images JPEG block analysis technique.

Camera images are amenable for manipulations by varied manageability’s and manovorabilities for desperate purposes in the crime scenario. The invention of advanced and modern techniques of reproduction such as photocopiers, scanners have led to the proliferation of original images available in original documents in one form or the other and consequently morphed images are entering in to the court rooms as evidences. Such instances are alarmingly increasing as the functionalities of image editing soft-wares allow an amateur to easily manipulate images. In some cases, they may be in the threshold of the requirements of a legal duplicate. In this paper, a case study of unearthing of such an entry of morphed images into court room in an attempt to derail justice is discussed. A technical approach evolved to detect digital alteration using image processing software Adobe Photoshop. The finding reveals that inconsistencies among the original and scanned images and their infringements have caused distortion. Experimental results demonstrate that scanned images are morphed .

Editing digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. Query is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugshot identification in which mugshot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known eigenface approach. Experiments have been conducted with real case images to evaluate the performance of both methods.

This paper deals with the offline signature verification system that is useful in automatic identification of signature forgeries involved in the bank, revenue and various other government documents. A set of features is extracted from the scanned image of the given sample of signature which is then applied to a neural network classifier to classify the sample into genuine, skilled forgery, unskilled forgery or random forgery. Artificially generated genuine and forgery samples from enrollment reference signatures are used to train the network, which allows definite training control and at the same time significantly reduces the number of enrollment samples required to achieve a good performance. The performance of such a verification system can be measured by deriving the values of two important performance indices. The false acceptance rate (FAR) is accepting forgery signature thinking it is a genuine signature and false rejection rate (FRR) which is rejecting the genuine signature thinking it is a forgery signature. The performance of the system is mainly influenced by the type of features chosen and the classifier that is selected. The Global features that are extracted from every pixel that lies within a rectangle circumscribing the signature, provide information about the specific cases of the signature shape. The Local or Grid features that are derived from the distribution of pixels over the segments or limited regions of the signature provide overall signature appearance information. Various classifiers have been tried in the literature; the important among them are Neural Network classifier, Fuzzy inference system, Baye’s classifier, Support Vector Machine etc. In the proposed system, a combination of global, and grid features are extracted from signature images for classifying them using neural network classifier based on back-propagation learning algorithm.

Text detection and extraction from scene images is useful in finding certain facts in supporting court of laws for solving crime problems and various forensic science applications. Text detection from scene images is a difficult and challenging problem due to various issues such as; varying font size, style, uneven illumination and other degradations. In this paper, a new method that uses wavelet energy features for text detection and extraction from scene images is presented. Initially, preprocessed binarized image will be divided into 50x50 blocks. Then, wavelet energy features are obtained from every block and potential text blocks are identified using newly defined discriminant functions. Further, the detected text blocks are merged and refined to extract text regions. The proposed method is robust and achieves a detection rate of 97% on a variety of scene images each of size 240x320.