Detector

Linguistics

Engineering

Philosophy

Statistics

Metallurgy

Materials science

Face (sociological concept)

Computer security

Literature

Telecommunications

Set (abstract data type)

Computer science

Initialization

Code (set theory)

Mechanical engineering

Organic chemistry

Pattern recognition (psychology)

Mathematics

Feature (linguistics)

Sociology

Face detection

Artificial intelligence

Matching (statistics)

Chemistry

Programming language

Dual (grammatical number)

Shot (pellet)

Feature extraction

Convolutional neural network

Facial recognition system

One shot

Computer vision

Key (lock)

Social science

Recently, Convolutional Neural Network (CNN) has achieved great success in face detection. However, it remains a challenging problem for the current face detection methods owing to high degree of variability in scale, pose, occlusion, expression, appearance and illumination. In this Paper, we propose a novel detection network named Dual Shot face Detector(DSFD). which inherits the architecture of SSD and introduces a Feature Enhance Module (FEM) for transferring the original feature maps to extend the single shot detector to dual shot detector. Specially, progressive anchor loss (PAL) computed by using two set of anchors is adopted to effectively facilitate the features. Additionally, we propose an improved anchor matching (IAM) method by integrating novel data augmentation techniques and anchor design strategy in our DSFD to provide better initialization for the regressor. Extensive experiments on popular benchmarks: WIDER FACE (easy: 0.966, medium: 0.957, hard: 0.904) and FDDB ( discontinuous: 0.991, continuous: 0.862 ) demonstrate the superiority of DSFD over the state-of-the-art face detection methods (e.g., PyramidBox and SRN). Code will be made available upon publication.

DSFD: Dual Shot Face Detector