Localized Deep-CNN Structure for Face Recognition
Adil Al-Azzawi, Jade Hind, Jianlin Jack Cheng · 2018
In face recognition, there currently exists a significant challenge, most prominent when complex conditions such as large expression, pose, illumination, and even low resolution are introduced. Therefore, there exists a demand to address this issue, particularly concerning the key challenge of efficient feature representation, appreciating the extensive feature space that can be utilized to improve efficiency. This paper proposes a new approach in which a localized Deep-CNN structure is applied to demonstrate its' effectiveness and efficiency. The main contribution of this model is to directly learn the localized visual features by splitting the learning face into four local blocks. Intuitively, the Localized Deep-CNN model mimics the primary visual context to joint feature representations by extraction to produce local relational visualized features for the learning face. Our Deep-CNN model achieves 97.13% accuracy rate on the Labeled Faces in the Wild (LFW) dataset, compared to the humanlevel recognition rate which is 98.76%.