From Facial Parts Responses to Face Detection: A Deep Learning Approach (1509.06451v1)

Abstract: In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variation, which are the main difficulty and bottleneck of most existing face detection approaches. We show that despite the use of DCN, our network can achieve practical runtime speed.

• The paper introduces a deep convolutional network that generates facial parts responses to enhance face detection under challenging conditions like occlusion and pose variations.
• It employs attribute-aware part detection and faceness scoring to reduce false positives while maintaining a high recall rate.
• Numerical results report a 90.99% recall on the FDDB benchmark, setting a new standard for robust face detection performance.

Deep Learning for Robust Face Detection: A Focus on Facial Parts

The paper "From Facial Parts Responses to Face Detection: A Deep Learning Approach" presents a sophisticated methodology for enhancing face detection capabilities using a novel deep convolutional network (DCN). By leveraging DCNs, the authors address significant challenges in face detection, such as severe occlusion and pose variations, and demonstrate substantial improvements over existing state-of-the-art methods.

Methodology Overview

The proposed method hinges on generating facial parts responses to enhance the face detection process. Key components of this approach include:

• Attribute-Aware Part Detection: The network utilizes attribute-aware deep networks to generate partness maps for various facial components—such as hair, eyes, nose, and mouth. This enables the detection of facial features even when faces are partially occluded.
• Faceness Scoring Mechanism: By analyzing the spatial configuration of detected facial parts, the network assigns a faceness score to candidate windows. This scoring mechanism allows for a significant reduction in false positives while maintaining high recall rates.
• Multistage Detection Pipeline: The initial stage focuses on generating high-quality face proposals, while a subsequent stage refines these proposals through a multitask DCN. This multi-layered approach ensures robust performance and precise localization of detected faces.

Numerical Results and Key Claims

The paper reports a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming previous methods by 2.91%. This robust performance is attributed to the novel use of partness maps and the faceness scoring approach, which effectively discerns face-like structures in images. The work addresses severe occlusion challenges, which have been a bottleneck in many traditional face detection techniques.

Implications and Future Directions

This research contributes to both theoretical and practical aspects of face detection in computer vision. The introduction of attribute-driven part detection offers a new perspective that could be generalized to other object detection tasks. Practically, the ability to detect faces under challenging conditions broadens the applicability of face recognition systems in surveillance, autonomous vehicles, and consumer electronics.

Future developments might explore further optimizations and extensions of this framework to enhance efficiency and scalability. Integrating advancements like low-rank approximations and model compression techniques could reduce computational overhead, making this method more suitable for deployment in real-time applications.

In conclusion, this paper exemplifies the power of deep learning in addressing complex pattern recognition problems, setting a new benchmark for face detection performance under challenging conditions. Researchers and practitioners in the field of computer vision can draw valuable insights from its methodology and findings.