K. Babu^1,*, C. Kumar², C. Kannaiyaraju³

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 317-329, 2022, DOI:10.32604/iasc.2022.023756

Abstract Facial expression for different emotional feelings makes it interesting for researchers to develop recognition techniques. Facial expression is the outcome of emotions they feel, behavioral acts, and the physiological condition of one’s mind. In the world of computer visions and algorithms, precise facial recognition is tough. In predicting the expression of a face, machine learning/artificial intelligence plays a significant role. The deep learning techniques are widely used in more challenging real-world problems which are highly encouraged in facial emotional analysis. In this article, we use three phases for facial expression recognition techniques. The principal component analysis-based dimensionality reduction techniques are… More >

Shumukh M. Aljuaid^*, Arshiya S. Ansari

Computer Systems Science and Engineering, Vol.41, No.3, pp. 1009-1025, 2022, DOI:10.32604/csse.2022.020785

Abstract This paper presents a novel method of a secured card-less Automated Teller Machine (ATM) authentication based on the three bio-metrics measures. It would help in the identification and authorization of individuals and would provide robust security enhancement. Moreover, it would assist in providing identification in ways that cannot be impersonated. To the best of our knowledge, this method of Biometric_ fusion way is the first ATM security algorithm that utilizes a fusion of three biometric features of an individual such as Fingerprint, Face, and Retina simultaneously for recognition and authentication. These biometric images have been collected as input data for… More >

Najlaa Hindi Alsaedi^*, Emad Sami Jaha

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1283-1311, 2022, DOI:10.32604/cmc.2022.021608

Abstract Biometric recognition refers to the process of recognizing a person’s identity using physiological or behavioral modalities, such as face, voice, fingerprint, gait, etc. Such biometric modalities are mostly used in recognition tasks separately as in unimodal systems, or jointly with two or more as in multimodal systems. However, multimodal systems can usually enhance the recognition performance over unimodal systems by integrating the biometric data of multiple modalities at different fusion levels. Despite this enhancement, in real-life applications some factors degrade multimodal systems’ performance, such as occlusion, face poses, and noise in voice data. In this paper, we propose two algorithms… More >

Khuram Nawaz Khayam¹, Zahid Mehmood^2,*, Hassan Nazeer Chaudhry³, Muhammad Usman Ashraf⁴, Usman Tariq⁵, Mohammed Nawaf Altouri⁶, Khalid Alsubhi⁷

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5039-5058, 2022, DOI:10.32604/cmc.2022.019975

Abstract Face recognition is a big challenge in the research field with a lot of problems like misalignment, illumination changes, pose variations, occlusion, and expressions. Providing a single solution to solve all these problems at a time is a challenging task. We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching. The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching and max-pooling. Finally, the input image… More >

Najlaa Hindi Alsaedi^*, Emad Sami Jaha

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 407-427, 2022, DOI:10.32604/iasc.2022.019538

Abstract Accurate recognition of person identity is a critical task in civil society for various application and different needs. There are different well-established biometric modalities that can be used for recognition purposes such as face, voice, fingerprint, iris, etc. Recently, face images have been widely used for person recognition, since the human face is the most natural and user-friendly recognition method. However, in real-life applications, some factors may degrade the recognition performance, such as partial face occlusion, poses, illumination conditions, facial expressions, etc. In this paper, we propose two dynamic feature subset selection (DFSS) methods to achieve better recognition for occluded… More >

Rohit Srivastava¹, Ravi Tomar¹, Ashutosh Sharma², Gaurav Dhiman³, Naveen Chilamkurti⁴, Byung-Gyu Kim^5,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1-19, 2021, DOI:10.32604/cmc.2021.015466

Abstract As multimedia data sharing increases, data security in mobile devices and its mechanism can be seen as critical. Biometrics combines the physiological and behavioral qualities of an individual to validate their character in real-time. Humans incorporate physiological attributes like a fingerprint, face, iris, palm print, finger knuckle print, Deoxyribonucleic Acid (DNA), and behavioral qualities like walk, voice, mark, or keystroke. The main goal of this paper is to design a robust framework for automatic face recognition. Scale Invariant Feature Transform (SIFT) and Speeded-up Robust Features (SURF) are employed for face recognition. Also, we propose a modified Gabor Wavelet Transform for… More >

Kazeem Oyebode¹, Shengzhi Du^2,*, Barend Jacobus van Wyk³

Computer Systems Science and Engineering, Vol.38, No.2, pp. 229-238, 2021, DOI:10.32604/csse.2021.015372

Abstract Context-aware facial recognition regards the recognition of faces in association with their respective environments. This concept is useful for the domestic robot which interacts with humans when performing specific functions in indoor environments. Deep learning models have been relevant in solving facial and place recognition challenges; however, they require the procurement of training images for optimal performance. Pre-trained models have also been offered to reduce training time significantly. Regardless, for classification tasks, custom data must be acquired to ensure that learning models are developed from other pre-trained models. This paper proposes a place recognition model that is inspired by the… More >

Jun Wang¹, Suncheng Feng^2,*, Yong Cheng³, Najla Al-Nabhan⁴

Journal of Information Hiding and Privacy Protection, Vol.3, No.1, pp. 29-45, 2021, DOI:10.32604/jihpp.2021.016835

Abstract With the continuous development of face recognition network, the selection of loss function plays an increasingly important role in improving accuracy. The loss function of face recognition network needs to minimize the intra-class distance while expanding the inter-class distance. So far, one of our mainstream loss function optimization methods is to add penalty terms, such as orthogonal loss, to further constrain the original loss function. The other is to optimize using the loss based on angular/cosine margin. The last is Triplet loss and a new type of joint optimization based on HST Loss and ACT Loss. In this paper, based… More >

Masoud Muhammed Hassan^1,*, Haval Ismael Hussein¹, Adel Sabry Eesa¹, Ramadhan J. Mstafa^1,2

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1637-1659, 2021, DOI:10.32604/cmc.2021.016467

Abstract Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis… More >

Yuetian Wang, Chuanjing Zhang, Xuxin Liao, Xingang Wang, Zhaoquan Gu^*

Journal on Artificial Intelligence, Vol.3, No.1, pp. 1-8, 2021, DOI:10.32604/jai.2021.014175

Abstract Deep neural networks (DNNs) are widely adopted in daily life and the security problems of DNNs have drawn attention from both scientific researchers and industrial engineers. Many related works show that DNNs are vulnerable to adversarial examples that are generated with subtle perturbation to original images in both digital domain and physical domain. As a most common application of DNNs, face recognition systems are likely to cause serious consequences if they are attacked by the adversarial examples. In this paper, we implement an adversarial attack system for face recognition in both digital domain that generates adversarial face images to fool… More >