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，亟需开发一种能够高效、有意义地融合多种生物特征的多模态系统。本文提出了一种名为"IdentiFace"的多模态面部生物特征识别系统，该系统将面部识别核心与性别、脸型、情感等关键软生物特征相结合。我们采用仅基于VGG-16架构的方案，在各子系统间进行微调，实现了跨模态的简化集成。该统一架构有助于解释任务间学习到的特征，从而揭示面部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]采集的五类高类内差异数据，测试准确率达99.2%；在性别识别任务中，本数据集准确率为99.4%，公开数据集[2]准确率为95.15%；基于名人脸型数据集[3]，脸型识别测试准确率达88.03%；最终，在FER2013数据集[4]的情感识别任务中，测试准确率为66.13%，相较于同类研究已属极佳水平。