I. INTRODUCTION

II. OVERVIEW
A. Components of Face Recognition

III. NETWORK ARCHITECTURE AND TRAINING LOSS
B. Evolution of Network Architecture
C. Face Matching by deep features

IV. FACE PROCESSING FOR TRAINING AND RECOGNITION
A. One-to-Many Augmentation
B. Many-to-One Normalization

V. FACE DATABASES AND EVALUATION PROTOCOLS
A. Large-scale training data sets
B. Training protocols
C. Evaluation tasks and performance metrics
D. Evaluation Scenes and Data

VI. DIVERSE RECOGNITION SCENES OF DEEP LEARNING
A. Cross-Factor Face Recognition
B. Heterogeneous Face Recognition
C. Multiple (or single) media Face Recognition
D. Face Recognition in Industry

VII. TECHNICAL CHALLENGES