Attention Enhanced Siamese Neural Network for Face Validation

Abstract

Few-shot computer vision algorithms have enormous potential to produce promised results for innovative applications which only have a small volume of example data for training. Currently, the few-shot algorithm research focuses on applying transfer learning on deep neural networks that are pre-trained on big datasets. However, adapting the transformers requires highly cost computation resources. In addition, the overfitting or underfitting problems and low accuracy on large classes in the face validation domain are identified in our  research. Thus, this paper proposed an alternative enhancement solution by adding contrasted attention to the negative face pairs and positive pairs to the training process. Extra attention is created through clustering-based face pair creation algorithms. The evaluation results show that the proposed approach sufficiently addressed the problems without requiring high-cost resources.

Received: 26 April 2023 | Revised: 4 July 2023 | Accepted: 26 July 2023

Conflicts of Interest

Hong Qing Yu is an associate editor for Artificial Intelligence and Applications, and was not involved in the editorial review or the decision to publish this article. The author declares that he has no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in Kaggle: https://www.kaggle.com/datasets/olgabelitskaya/yale-face-database and https://www.kaggle.com/datasets/jessicali9530/lfw-dataset.