Criminal Identification Based on Androgenic Hair Pattern Using KNN-Clustering Method

This paper is implemented in order to identify the criminals in forensics based on the androgenic hair pattern of the criminal particularly in Low-resolution images. The Proposed paper is more dependable than the existing forensic techniques to identify criminals. The paper is implemented in five stages; in the first stage the input images and the database images are obtained after which the database images are trained through the algorithm to get the trained images. In the second stage pre-processing of the images is done in order to convert the image to grey scale and remove noise using thresholding method. The Third stage the image is passed through a Gabor filter to detect the edges in the image, this is followed by the Fourth phase where KNN clustering method is used to obtain the region of interest followed by the application of the bilateral filter in order to enhance the image. In the final stage the indifferent value calculated from the input image is matched with all the indifferent values of the trained images to identify the criminal

Biometric identification using augmented database

Androgenic hair pattern is one of the newest soft biometric trait that can be used to identify criminals when their faces are covered in the evidences of criminal investigation. In real-life situation, sometimes the available evidence is limited thus creating problems for authorities to identify criminal based on the limited data. This research developed the recognition system to identify individuals based on their androgenic hair pattern in a limited data situation in such a way that the limited images were expanded by the augmentation process. There were 50 images studied and expanded into 2.000 images from the augmentation process of rotating, reflecting, adjusting color and intensity. Furthermore, the effect of human skin color extraction was investigated by employing HSV and YCbCr color spaces. The scale-space hierarchy was built among the images with Gaussian function and produced 70% recognition precision that was around more than 2 times higher compared to system of recognition with only limited data

Hierarchical Gaussian Scale-Space on Androgenic Hair Pattern Recognition

Androgenic hair pattern stated to be the new biometric trait since 2014. The research to improve the performance of androgenic hair pattern recognition system has begun to be developed due to the problems that occurred when other apparent biometric trait such as face is hidden from sight. The recognition system was built with hierarchical Gaussian scale-space using 4 octaves and 3 levels in each octave. The system also implemented the equalization process to adjust image’s intensity by using histogram equalization. We analyzed 400 images of androgenic hair in the database that were analyzed using 2-fold and 10-fold cross validation and Euclidean distance to classify it. The experimental results showed that our proposed method gave better performance compared to previous work that used Haar wavelet transformation and principal component analysis as the main method. The best recognition precision was 94.23 % obtained from the base octave with the third level using histogram equalization and 10-fold cross validation. 

Palmprint Recognition in Uncontrolled and Uncooperative Environment

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is end-to-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods.Comment: Accepted in the IEEE Transactions on Information Forensics and Securit