Forensic Face Sketch Recognition Using Computer Vision

Now - a - days need for technologies for identification, detection and recognition of suspects has increased. One of the most common biometric techniques is face recognition, since face is the convenient way used by the people to identify each - other. Understanding how humans recognize face sketches drawn by artists is of significant value to both criminal investigators and forensic researchers in Computer Vision. However studies say that hand - drawn face sketches are still very limited in terms of artists and number of sketches because after any incident a forensic artist prepares a victims sketches on behalf of the descripti on provided by an eyewitness. Sometimes suspects used special mask to hide some common features of faces like nose, eyes, lips, face - color etc. but the outliner features of face biometrics one could never hide. In this work, I concentrated on some specific facial geometric feature which could be used to calculate some ratios of similarities from the template photograph database against the forensic sketches. This paper describes the design of a system for forensic face sketch recognition by a computer visi on approach like Two - Dimensional Discrete Cosine Transform (2D - DCT) and the Self - Organizing Map (SOM) Neural Network simulated in MATLAB

An Enhanced Computer Vision By Using MLP Approach To Forensic Face Sketch Recognition System‎

Technologies for suspect identification, detection, and recognition have become more critical in recent years. As a result, face recognition is an almost commonly used biometric technique. Investigators for Criminal and forensic computer vision researchers are interested in the human-recognized face sketches were drawn by artists. Hand-drawn face sketches are, according to studies, ‎still extremely rare, both in terms of artists and number of drawings, since forensic artists ‎prepare victim drawings based on descriptions were provided by eyewitnesses following an incident‎. Masks are sometimes used to conceal standard facial features such as noses, eyes, lips, and skin color, but face biometrics' outliner features are impossible to conceal. This paper concentrated on a particular face-geometrical feature that could calculate some similarity ratios between composite template photos and forensic sketches. Computer vision techniques such as Two-Dimensional Discrete Cosine Transform (2D-DCT) and the Self-Organizing Map (SOM) Neural Network are used to design a system for composite and forensic face sketch recognition

Face Recognition: Issues, Methods and Alternative Applications

Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention. It is due to availability of feasible technologies, including mobile solutions. Research in automatic face recognition has been conducted since the 1960s, but the problem is still largely unsolved. Last decade has provided significant progress in this area owing to advances in face modelling and analysis techniques. Although systems have been developed for face detection and tracking, reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers. There are several reasons for recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world, face analysis and modelling techniques in multimedia data management and computer entertainment. In this chapter, we have discussed face recognition processing, including major components such as face detection, tracking, alignment and feature extraction, and it points out the technical challenges of building a face recognition system. We focus on the importance of the most successful solutions available so far. The final part of the chapter describes chosen face recognition methods and applications and their potential use in areas not related to face recognition

Delving Deep into the Sketch and Photo Relation

"Sketches drawn by humans can play a similar role to photos in terms of conveying shape, posture as well as fine-grained information, and this fact has stimulated one line of cross-domain research that is related to sketch and photo, including sketch-based photo synthesis and retrieval. In this thesis, we aim to further investigate the relationship between sketch and photo. More specifically, we study certain under- explored traits in this relationship, and propose novel applications to reinforce the understanding of sketch and photo relation.Our exploration starts with the problem of sketch-based photo synthesis, where the unique trait of non-rigid alignment between sketch and photo is overlooked in existing research. We then carry on with our investigation from a new angle to study whether sketch can facilitate photo classifier generation. Building upon this, we continue to explore how sketch and photo are linked together on a more fine-grained level by tackling with the sketch-based photo segmenter prediction. Furthermore, we address the data scarcity issue identified in nearly all sketch-photo-related applications by examining their inherent correlation in the semantic aspect using sketch-based image retrieval (SBIR) as a test-bed. In general, we make four main contributions to the research on relationship between sketch and photo.Firstly, to mitigate the effect of deformation in sketch-based photo synthesis, we introduce the spatial transformer network to our image-image regression framework, which subtly deals with non-rigid alignment between the sketches and photos. The qualitative and quantitative experiments consistently reveal the superior quality of our synthesised photos over those generated by existing approaches.Secondly, sketch-based photo classifier generation is achieved with a novel model regression network, which maps the sketch to the parameters of photo classification model. It is shown that our model regression network is able to generalise across categories and photo classifiers for novel classes not involved in training are just a sketch away. Comprehensive experiments illustrate the promising performance of the generated binary and multi-class photo classifiers, and demonstrate that sketches can also be employed to enhance the granularity of existing photo classifiers.Thirdly, to achieve the goal of sketch-based photo segmentation, we propose a photo segmentation model generation algorithm that predicts the weights of a deep photo segmentation network according to the input sketch. The results confirm that one single sketch is the only prerequisite for unseen category photo segmentation, and the segmentation performance can be further improved by utilising sketch that is aligned with the object to be segmented in shape and position.Finally, we present an unsupervised representation learning framework for SBIR, the purpose of which is to eliminate the barrier imposed by data annotation scarcity. Prototype and memory bank reinforced joint distribution optimal transport is integrated into the unsupervised representation learning framework, so that the mapping between the sketches and photos could be automatically detected to learn a semantically meaningful yet domain-agnostic feature space. Extensive experiments and feature visualisation validate the efficacy of our proposed algorithm.

Modeling and Mapping Location-Dependent Human Appearance

Human appearance is highly variable and depends on individual preferences, such as fashion, facial expression, and makeup. These preferences depend on many factors including a person\u27s sense of style, what they are doing, and the weather. These factors, in turn, are dependent upon geographic location and time. In our work, we build computational models to learn the relationship between human appearance, geographic location, and time. The primary contributions are a framework for collecting and processing geotagged imagery of people, a large dataset collected by our framework, and several generative and discriminative models that use our dataset to learn the relationship between human appearance, location, and time. Additionally, we build interactive maps that allow for inspection and demonstration of what our models have learned