China 
Africa 
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Introduction
Published in Ravindra Das, Adopting Biometric Technology, 2017
Of all the biometric technologies that are available today, it is facial recognition which is the most susceptible to privacy rights and civil liberties issues. Its rate has actually diminished over time, but back in the last decade, it was at all-time highs. After the incidents of 9/11 occurred, facial recognition received all of the hype and attention that it would be the ultimate security solution. Just like the dot-com boom of the late 1990s, the biometrics industry, with facial recognition at the forefront, was experiencing the same type of trend. Venture capitalists were pouring money into the industry, and there emerged a plethora of new bio-metric vendors. But just like the dot-com boom, the biometrics industry also faced its huge and climactic downturn. For instance, facial recognition simply could not live up to the hype it received. The modality simply did not live up to the performance metrics as it was promised by the biometric vendors. It could neither verify nor identify end users without large FAR as well as FRR. As a result, the media greatly criticized the viability and sustainability of facial recognition as a viable means to track down and capture terror suspects. On account of this, the American public started to have enormous misgivings and a lack of trust about facial recognition, thus triggering its association with privacy rights and civil liberties violations.
AI in Healthcare
Published in Shivani Agarwal, Sandhya Makkar, Duc-Tan Tran, Privacy Vulnerabilities and Data Security Challenges in the IoT, 2020
The field has become multifarious and scandalous as a great misuse of personal data occurred through Facebook. Despite all this, facial recognition will grow as the biggest field in the arena as the applications are hugely wide-ranging. The readability and accuracy of AI applications using facial recognition has benefitted a lot from the use of facial recognition in defense, at airports, and in various banking sectors where public access needs to be secure. Research into and the implementation of facial recognition will ultimately benefit defense and security agencies in tracking hackers, terrorists, criminals, and rogue elements in society; and businesses which need to provide more personalized data services to customers.
Blockchain-Based Regenerative E-Voting System
Published in Sudhir Kumar Sharma, Bharat Bhushan, Bhuvan Unhelkar, Security and Trust Issues in Internet of Things, 2020
S. Vishnuvardhan, R. Aswath Srimari, S. Sridevi, B. Vinoth Kumar, G. R. Karpagam
Election commission employees are given the privilege to oversee the voting procedure. Election commission employees will log in to the system with their electronic mail ID with a secure password. The voting system requires the facial recognition of the employees to allow access to the database. Facial recognition is one of the biometric software that maps individual facial characteristics mathematically and stores it in database as a faceprint. The individual is identified by matching the facial features captured using the camera with the faceprint stored in a database.
Gendered Innovations: integrating sex, gender, and intersectional analysis into science, health & medicine, engineering, and environment
Published in Tapuya: Latin American Science, Technology and Society, 2021
Here I highlight several of our new case studies beginning with our case study on facial recognition. Like other technologies reliant on big data, facial recognition systems can perpetuate, and even amplify, social injustices by consciously or unconsciously encoding human bias. A now well-known study, Gender Shades by Joy Boulamwini and Timnit Gebru, measured the accuracy of commercial facial recognition systems – including those from Microsoft, IBM, and Face++ – and found that these systems performed better on men’s faces than on women’s faces (sex analysis), and that all systems performed better on lighter-skin than darker-skin (race analysis). When they considered how sex and race intersect, they saw that error rates were 35% for darker-skinned women, 12% for darker-skinned men, 7% for lighter-skinned women, and less than 1% for lighter-skinned men (Buolamwini and Gebru 2018). The fix? The team developed and labeled a new intersectional dataset to test sex and race classification performance on four subgroups: darker-skinned women, darker-skinned men, lighter-skinned women, and lighter-skinned men. Their dataset consisted of 1270 images from three African countries (Rwanda, Senegal, and South Africa) and three European countries (Iceland, Finland, and Sweden). Using this or similar new datasets, all commercial systems improved accuracy (Raji and Buolamwini 2019).
An LDOP approach for face identification under unconstrained scenarios
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
Rinku Datta Rakshit, Ajita Rattani, Dakshina Ranjan Kisku
Using digital images or video frames from a video source, a facial recognition system can verify or identify a person (Jain & Li, 2011). While the input face image is compared to the template of the claimed identity in the verification mode, the input face image is matched to the templates of all the individuals who have been enrolled in the dataset for identity inference in the identification mode. Face recognition is a technique that is widely used in surveillance, border control, healthcare, banking services and, most recently, mobile user identification with Apple’s ‘Face ID’ name with iPhone X (Hirayama et al., 2019).
Gender and Age Classification Enabled Blockschain Security Mechanism for Assisting Mobile Application
Published in IETE Journal of Research, 2021
Sapna Juneja, Sourav Jain, Aparna Suneja, Gurminder Kaur, Yasser Alharbi, Ali Alferaidi, Abdullah Alharbi, Wattana Viriyasitavat, Gaurav Dhiman
Traditional mobile and application security measures use strong passwords to enhance the security of the application, but there are some drawbacks of this approach which include the risk of stealing the password and the inconvenience of remembering the password for the actual user. Two-factor authentications are a better alternative that uses strong passwords coupled with biometric features to prevent unauthorised access [1–3]. Biometric methods are categorised into two classes as Behavioural method and Physiological method. Physiological biometric refers to physical measurements of the human body like finger-mark recognition, face recognition and retina recognition, while Behavioural biometric is dependent upon the measurements of human’s actions which includes gesture, key stroking, and signature recognition [4]. In this paper, a Face Lock algorithm along with age and gender classifier is designed using Machine Learning Technique which is based on Face Recognition. Facial recognition algorithm, first of all, detects face from an image, analyses the face based on the structure of the face of the person such as interspace among the eyes, height, and width of the face, face colour, etc. Then the algorithm transforms the analogue information (face) into digital data. Face analysis is converted into numerical code called faceprint, which is further being compared with the available database that contains the description of various individual’s faces. If the print of the face seems similar to some image in the database, then a determination is made [5–7]. Hence the main steps in face recognition algorithm are face detection, feature extraction, and face recognition. The challenges experienced during face recognition are illumination conditions, scale, occlusion, etc. Occlusion often occurs when two or more objects come too close and merge with each other. Various algorithms have been designed to deal with these challenges [8–10].