The development of facial biometric systems has contributed greatly to the development of the computer vision field. Nowadays, there's always a need to develop a multimodal system that combines multiple biometric traits in an efficient, meaningful way. In this paper, we introduce "IdentiFace" which is a multimodal facial biometric system that combines the core of facial recognition with some of the most important soft biometric traits such as gender, face shape, and emotion. We also focused on developing the system using only VGG-16 inspired architecture with minor changes across different subsystems. This unification allows for simpler integration across modalities. It makes it easier to interpret the learned features between the tasks which gives a good indication about the decision-making process across the facial modalities and potential connection. For the recognition problem, we acquired a 99.2% test accuracy for five classes with high intra-class variations using data collected from the FERET database[1]. We achieved 99.4% on our dataset and 95.15% on the public dataset[2] in the gender recognition problem. We were also able to achieve a testing accuracy of 88.03% in the face-shape problem using the celebrity face-shape dataset[3]. Finally, we achieved a decent testing accuracy of 66.13% in the emotion task which is considered a very acceptable accuracy compared to related work on the FER2013 dataset[4].

翻译：面部生物特征识别系统的发展极大地推动了计算机视觉领域的进步。当前，开发一种能够有效且有意义地融合多种生物特征的多模态系统始终是迫切需求。本文提出"IdentiFace"——一种将人脸识别核心与性别、脸型、表情等关键软生物特征相结合的多模态面部生物特征识别系统。我们着重采用仅基于VGG-16架构并针对不同子系统进行微调的设计方案。这种统一架构简化了各模态间的集成，使得跨任务学习特征的分析更加直观，从而揭示面部多模态决策过程及其潜在关联。在识别任务中，基于FERET数据库[1]收集的五类高类内差异数据，我们取得了99.2%的测试准确率。在性别识别任务中，我们获得了99.4%（自有数据集）和95.15%（公共数据集[2]）的准确率。基于名人脸型数据集[3]的面型识别任务测试准确率达88.03%。最后，在表情识别任务中，我们取得了66.13%的测试准确率，相较于FER2013数据集[4]的相关研究，该结果处于可接受的精度范围。