Soft Biometrics and Deep Learning: Detecting Facial Soft Biometrics Features Using Ocular and Forehead Region for Masked Face Images

Soft Biometrics is a growing field that has been known to improve the recognition system as witnessed in the past decade. When combined with hard biometrics like iris, gait, fingerprint recognition etc. it has been seen that the efficiency of the system increases many folds. With the Pandemic came the need to recognise faces covered with mask in an efficient way- soft biometrics proved to be an aid in this. While recent advances in computer vision have helped in the estimation of age and gender - the system could be improved by extending the scope and detecting quite a few other soft biometric attributes that helps us in identifying a person, including but not limited to - eyeglasses, hair type and color, mustache, eyebrows etc. In this paper we propose a system of identification that uses the ocular and forehead part of the face as modalities to train our models that uses transfer learning techniques to help in the detection of 12 soft biometric attributes (FFHQ dataset) and 25 soft biometric attributes (CelebA dataset) for masked faces. We compare the results with the unmasked faces in order to see the variation of efficiency using these data-sets Throughout the paper we have implemented 4 enhanced models namely - enhanced Alexnet ,enhanced Resnet50, enhanced MobilenetV2 and enhanced SqueezeNet. The enhanced models apply transfer learning to the normal models and aids in improving accuracy. In the end we compare the results and see how the accuracy varies according to the model used and whether the images are masked or unmasked. We conclude that for images containing facial masks - using enhanced MobileNet would give a splendid accuracy of 92.5% (for FFHQ dataset) and 87% (for CelebA dataset).

Core point pixel-level localization by fingerprint features in spatial domain

<abstract> <p>Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples.</p> </abstract>

Multifactor Authentication in Automated Teller Machine

Skimming of card details is the primary problem faced by many people in today’s world. This can be done in many ways. For instance, a thief can insert a small device into the machine and steal the information. When a person swipes or inserts a card, the details will be captured and stored. This problem can be solved using biometrics. Biometrics include fingerprint, iris, face, retina scanning, etc. This paper focused on solving this issue using fingerprint and iris recognition using OpenCV and propose a suitable method for this issue. Fingerprint and iris recognition are performed by identifying the keypoints and descriptors and matching those with the test data. Keywords: Biometrics, Fingerprint recognition, Iris recognition, Scale Invariant Feature Transform, Oriented FAST and Rotated BRIEF, OpenCV

Hough Transform for Distinctive Edge Detection to Images in Fingerprint Recognition Matching Transformation

Biometric Identification is a globally renowned procedure, which has been utilised to achieve a successful and accurate level of identification. In the sea of biometrics, fingerprints are deemed more popular when it comes to verification. This results from the presence of the ridges on the fingerprints that are completely exclusive to each individual. Besides that, fingerprints are expansively employed to ascertain and authenticate people individually. Therefore, this study had proposed to employ distinctive Edge Detection techniques together with the Hough Transform to match the images of the fingerprints in a fingerprint matching system. The Hough Transform is a superior procedure carried out to get an accurate series of finer points or lines. The finer points or lines would then distinguish the fingerprints. Nevertheless, it was still a challenge to extract finer points or lines from the fingerprints under uninhibited conditions. Therefore, this paper was organised based on four distinctive steps. First, different Edge Detection operators were employed to perform the fingerprint matching algorithm. Next, the fingerprint matching algorithm was applied twice to the same Edge Detection operators. Thirdly, the Edge Detection operators had been substituted with the Transformation Method for the same matching procedure. For example, the proposed fingerprint matching algorithm comprised of the Hough Transform and same Edge Detection operators. Finally, distinct Edge Detection operators based on the decision making algorithm were used to calculate and determine the percentage of matching. Therefore, this study proved that the prints obtained via the Prewitt Edge Detection together with Hough Transform were in an agreement.

Design of Bluetooth Automatic Check in System Without Operation Based on Cross Mobile Platform

Because the traditional way of sign in is not to call the roll, or sign in on the paper list, or sign in through fingerprint recognition and face recognition, on the one hand, it is cumbersome and time-consuming, on the other hand, it is non-human. The update of roll call sign in is a hot topic at present, and many new schemes rush out. This paper introduces the automatic sign in system based on Bluetooth technology, which does not rely on the mobile phone operating system, does not need the mobile phone installation software, as long as you bring a mobile phone, you can realize automatic sign in. This paper designs and implements the automatic check-in system, and compares it with other ways, proving that it is an innovative check-in method.

Person Verification Based on Multimodal Biometric Recognition

Nowadays, person recognition has received significant attention due to broad applications in the security system. However, most person recognition systems are implemented based on unimodal biometrics such as face recognition or voice recognition. Biometric systems that adopted unimodal have limitations, mainly when the data contains outliers and corrupted datasets. Multimodal biometric systems grab researchers’ consideration due to their superiority, such as better security than the unimodal biometric system and outstanding recognition efficiency. Therefore, the multimodal biometric system based on face and fingerprint recognition is developed in this paper. First, the multimodal biometric person recognition system is developed based on Convolutional Neural Network (CNN) and ORB (Oriented FAST and Rotated BRIEF) algorithm. Next, two features are fused by using match score level fusion based on Weighted Sum-Rule. The verification process is matched if the fusion score is greater than the pre-set threshold. The algorithm is extensively evaluated on UCI Machine Learning Repository Database datasets, including one real dataset with state-of-the-art approaches. The proposed method achieves a promising result in the person recognition system.