Tayyaba Farhat^1,2, Sheeraz Akram^3,*, Hatoon S. AlSagri³, Zulfiqar Ali⁴, Awais Ahmad³, Arfan Jaffar^1,2

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 105-126, 2024, DOI:10.32604/cmc.2023.045022 - 30 January 2024

Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by significant challenges in social interaction, communication, and repetitive behaviors. Timely and precise ASD detection is crucial, particularly in regions with limited diagnostic resources like Pakistan. This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context. The research involves experimentation with VGG16 and MobileNet models, exploring different batch sizes, optimizers, and learning rate schedulers. In addition, the “Orange” machine learning tool is employed to… More >

Puja S. Prasad¹, Adepu Sree Lakshmi¹, Sandeep Kautish², Simar Preet Singh³, Rajesh Kumar Shrivastava³, Abdulaziz S. Almazyad⁴, Hossam M. Zawbaa⁵, Ali Wagdy Mohamed^6,7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 725-739, 2024, DOI:10.32604/cmes.2023.030640 - 30 December 2023

Abstract Pupil dynamics are the important characteristics of face spoofing detection. The face recognition system is one of the most used biometrics for authenticating individual identity. The main threats to the facial recognition system are different types of presentation attacks like print attacks, 3D mask attacks, replay attacks, etc. The proposed model uses pupil characteristics for liveness detection during the authentication process. The pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light intensities. The proposed framework consists of two-phase methodologies. In the first phase, the pupil’s diameter is calculated by applying… More >

Valliappan Raman¹, Khaled ELKarazle^2,*, Patrick Then²

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1341-1355, 2023, DOI:10.32604/csse.2023.026674 - 15 June 2022

Abstract Given the current expansion of the computer vision field, several applications that rely on extracting biometric information like facial gender for access control, security or marketing purposes are becoming more common. A typical gender classifier requires many training samples to learn as many distinguishable features as possible. However, collecting facial images from individuals is usually a sensitive task, and it might violate either an individual's privacy or a specific data privacy law. In order to bridge the gap between privacy and the need for many facial images for deep learning training, an artificially generated dataset… More >

Zia Ullah¹, Muhammad Ismail Mohmand¹, Sadaqat ur Rehman^2,*, Muhammad Zubair³, Maha Driss⁴, Wadii Boulila⁵, Rayan Sheikh², Ibrahim Alwawi⁶

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4465-4487, 2022, DOI:10.32604/cmc.2022.029101 - 28 July 2022

Abstract Facial expression recognition has been a hot topic for decades, but high intraclass variation makes it challenging. To overcome intraclass variation for visual recognition, we introduce a novel fusion methodology, in which the proposed model first extract features followed by feature fusion. Specifically, RestNet-50, VGG-19, and Inception-V3 is used to ensure feature learning followed by feature fusion. Finally, the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification. The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy. To evaluate the efficiency More >