Computer Vision of Smile Detection Based on Machine and Deep Learning Approach

Smile detection and recognition have been a key component of sentiment analysis, social robotics, human-computer interaction, and mental health monitoring before the advent of deep learning. Understanding and accurately identifying smiles can provide deep insights into human behavior, strengthen communication systems, and enhance adaptive responses in AI interfaces. This paper is a comprehensive review of algorithms developed for smile detection and recognition, and categorizes their main approaches into three traditional computer vision techniques: feature-based, machine learning-based, and deep learning-based. These techniques rely on handcrafted features such as edges, geometric features of the face, and texture, which give interpretability and limited adaptability. This paper explores feature extraction methods such as geometric and histogram-based features (e.g., histograms of directed gradients). In addition, this paper evaluates the effectiveness of traditional classifiers, including support vector machines that use machine learning-based methods, leveraging algorithms such as support vector machines (SVMs), extracted features to classify smiles with improved accuracy. Deep learning techniques, especially convolutional neural networks (CNNs) and hybrid methods provide end-to-end learning capabilities, extracting features directly from raw pixel data and enabling real-time performance. These frameworks, including recurrent neural networks (RNNs) for temporal analysis, generative adversarial networks (GANs) for data augmentation, and graph neural networks (GNNs) for structural analysis, have also pushed the boundaries of smile detection in dynamic and challenging environments. It also aims to provide a comprehensive overview of these classical methods, and analyze their strengths, limitations, drawbacks, and performance across diverse datasets of the proposed databases by focusing on describing these datasets and researchers’ methods of working on them as benchmarks for their research, and highlighting their importance in the environments and their contributions to the development of smile detection algorithms in the field of computer vision. Among these datasets are datasets such as CK+, FER2013, AffectNet, and Jaffe in developing, training, and evaluating smile detection and recognition algorithm models. By comparing these methodologies, our paper recommends directing future research towards more efficient, robust, and scalable solutions for smile detection and recognition in diverse applications.