Human mind processes the different primitive components of image signals such as color, shape, texture, and symmetry in a parallel and complex fashion. Deep neural networks aim to learn all these components from the image in an unsupervised manner. However, learning the primitive features is not formally assured in a deep learning formulation, and, adding these features explicitly would improve the performance. Especially in face recognition, humans intuitively and implicitly employ the usage of primitive features such as color, shape, texture, and symmetry of faces. Inspired by this observation, this paper presents a novel approach in building a learning based TeCS${}^2$ space. This space consists of meta-level features obtained from dictionary learning and combining it with task specific deep learning classifiers (such as DenseNet) for face recognition. Confidence based fusion mechanism is presented to supplement the task specific deep learning classifier with the proposed TeCS${}^2$ features. The effectiveness of the proposed framework is evaluated on four benchmark face recognition datasets: (i) Disguised Faces in the Wild (DFW), (ii) Labeled faces in the wild (LFW), (iii) IIITD Plastic Surgery dataset, and (iv) Point and Shoot Challenge (PaSC).

人脑以并行和复杂的方式处理图像信号的不同原始成分，例如颜色，形状，纹理和对称性。深度神经网络旨在以无监督的方式从图像中学习所有这些成分。但是，在深度学习公式中不能正式保证学习原始功能，并且明确添加这些功能将提高性能。尤其是在人脸识别中，人类会直观，隐式地使用原始特征（例如颜色，形状，纹理和人脸对称性）的用法。受此观察启发，本文提出了一种构建基于学习的TeCS的新颖方法${}^2$空间。该空间包含从字典学习中获得的元级别功能，并将其与特定于任务的深度学习分类器（例如DenseNet）相结合以进行人脸识别。提出了基于置信度的融合机制，以使用拟议的TeCS补充任务特定的深度学习分类器${}^2$特征。在四个基准人脸识别数据集上评估了所提出框架的有效性：（i）野外变相人脸（DFW），（ii）野外有标签人脸（LFW），（iii）IIITD整形外科数据集和（iv ）指向射击挑战赛（PaSC）。