A New Framework for Matching Forensic Composite Sketches With Digital Images

Face sketch recognition is considered as a sub-problem of face recognition. Matching composite sketches with its corresponding digital image is one of the challenging tasks. A new convolution neural network (CNN) framework for matching composite sketches with digital images is proposed in this work. The framework consists of a base CNN model that uses swish activation function in the hidden layers. Both composite sketches and digital images are trained separately in the network by providing matching pairs and mismatching pairs. The final output resulted from the network's final layer is compared with the threshold value, and then the pair is assigned to the same or different class. The proposed framework is evaluated on two datasets, and it exhibits an accuracy of 78.26% with extended-PRIP (E-PRIP) and 69.57% with composite sketches with age variations (CSA) respectively. Experimental analysis shows the improved results compared to state-of-the-art composite sketch matching systems.

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 victim’s sketches on behalf of the description provided by an eyewitness. Sometimes suspect uses 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. Here we concentrate on some specific facial geometric feature which could be used to calculate some ratio of similarities from the template photograph database against the forensic sketches. The project describes the design of a system for face sketch recognition by a computer vision approach like Discrete Cosine Transform (DCT), Local Binary Pattern Histogram (LBPH) algorithm and a supervised machine learning model called Support Vector Machine (SVM) for face recognition. Tkinter is the standard GUI library for Python. Python when combined with Tkinter provides a fast and easy way to create GUI applications. Tkinter provides a powerful object-oriented interface to the Tk GUI toolkit.

Face sketch recognition using principal component analysis for forensics application

Recognition of human faces in forensics applications can be identified through the Sketch recognition method by matching sketches and photos. The system gives five criminal candidates who have similarities to the sketch given. This study aims to perform facial recognition on photographs and sketches using Principal Component Analysis (PCA) as feature extraction and Euclidean distance as a calculation of the distance of test images to training images. The PCA method was used to recognize facial images from pencil sketch drawings. The system dataset is in the form of photos and sketches in the CUHK Face Sketch database consists of 93 photos and 93 sketches, and personal documentation consists of five photos and five sketches. The sketch matching application to training data produces an accuracy of 76.14 %, precision of 91.04 %, and recall of 80.26 %, while testing with sketch modifications produces accuracy and recall of 95 % and precision of 100 %.