用机器学习从电影人脸图预测拍摄年份，提升影视识别精度。
Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning
- 从阿拉伯电影中提取人脸，结合多模型特征提取与分类
- 逻辑回归模型在测试中达99%准确率，各项指标领先
- 适用于影视搜索、推荐与类型分类，对弱光照场景有效
本研究采用机器学习算法，从随机选取的电影图像中识别演员并提取其年龄。所用图像来自阿拉伯电影，面临光照不均、姿态多样、背景复杂以及化妆、假发、胡须、配饰和服装变化等挑战，增加了同一演员身份识别的难度。研究构建了包含574张图像的阿拉伯演员数据集（AAD），涵盖黑白与彩色画面，图像内容为完整场景或片段。实验中采用多种特征提取模型和机器学习算法进行分类与预测，以确定最优方案。结果显示，逻辑回归模型在训练阶段表现最佳，其AUC、精确率、准确率和F1分数分别为99%、86%、85.5%和84.2%。研究成果可提升电影相关人脸识别技术的精度与可靠性，应用于影片搜索服务、推荐系统及电影类型分类。
原文摘要 · Abstract (English)
This study used machine learning algorithms to identify actors and extract the age of actors from images taken randomly from movies. The use of images taken from Arab movies includes challenges such as non-uniform lighting, different and multiple poses for the actors and multiple elements with the actor or a group of actors. Additionally, the use of make-up, wigs, beards, and wearing different accessories and costumes made it difficult for the system to identify the personality of the same actor. The Arab Actors Dataset-AAD comprises 574 images sourced from various movies, encompassing both black and white as well as color compositions. The images depict complete scenes or fragments thereof. Multiple models were employed for feature extraction, and diverse machine learning algorithms were utilized during the classification and prediction stages to determine the most effective algorithm for handling such image types. The study demonstrated the effectiveness of the Logistic Regression model exhibited the best performance compared to other models in the training phase, as evidenced by its AUC, precision, CA and F1score values of 99%, 86%, 85.5% and 84.2% respectively. The findings of this study can be used to improve the precision and reliability of facial recognition technology for various uses as with movies search services, movie suggestion algorithms, and genre classification of movies.
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