Work

Deep learning-based evaluation system for assessing actor performance using facial images

Year: 2026

Type: article

Abstract: In cinema, audiences are often mesmerized by the performance of actors/actresses due to their seamless acting skills. Despite enacting every expression aptly, sometimes these performers have to face bias in the industry, be it nepotism, unfair opportunities or any other factors. In this research article, we are proposing a model built on the foundations of Convolutional Neural Network (CNN) and Vision Transformer (ViT) for analyzing the performance of actors based on their facial expressions for a particular scenario in an unbiased manner. The architecture of the proposed model includes a convolution layer, max pooling, GeLU activation function, augmentation, batch normalization and an intermediate Vision Transformer. Two open-access datasets CK + and KDEF have been employed for training and testing of the proposed model CNN_ViT, to achieve an accuracy of 97.99% and 96.59% on the aforementioned datasets. The results indicate that the proposed model, CNN_ViT, outperformed other machine learning models in terms of accuracy while evaluating the performance.

Source: Discover Artificial Intelligence

DOI: 10.1007/s44163-026-01508-5

Authors: H. Varun Chand, Seema Sabharwal, Weiwei Jiang, Syarul Azlina Sikandar, Edeh Michael Onyema, Oby Modest Ogbuoka

Institutions: APJ Abdul Kalam Technological University, Kurukshetra University, Beijing University of Posts and Telecommunications, Universiti Teknologi MARA System, Universiti Teknologi MARA, Enugu State University of Science and Technology, Saveetha University

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,590

OpenAlex ID: W7170132946

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