Design and VLSI implementation of high-performance face-detection engine for mobile applications

2011 ◽  
Author(s):  
Dongil Han ◽  
Jongho Choi ◽  
Jae-Il Cho ◽  
Dongsu Kwak
Keyword(s):  
Face Detection ◽  
Mobile Applications ◽  
High Performance ◽  
Vlsi Implementation

Design and VLSI implementation of a high-performance face detection engine

2012 ◽  
Vol 38 (5) ◽  
pp. 1222-1239 ◽  
Author(s):  
Dongil Han ◽  
Jongho Choi ◽  
Byungwhan Kim ◽  
Jae Il Cho
Keyword(s):  
Face Detection ◽  
High Performance ◽  
Vlsi Implementation

scholarly journals A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection

2018 ◽  
Vol 10 (8) ◽  
pp. 80
Author(s):  
Lei Zhang ◽  
Xiaoli Zhi
Keyword(s):  
Face Detection ◽  
Graphics Processing Units ◽  
High Performance ◽  
Feature Fusion ◽  
Local Context ◽  
Data Set ◽  
Global Context ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Benchmark Datasets

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.

Planar fully depleted SOI technology: The convergence of high performance and low power towards multimedia mobile applications

2012 ◽  
Author(s):  
Bertrand Pelloux-Prayer ◽  
Milovan Blagojevic ◽  
Olivier Thomas ◽  
Amara Amara ◽  
Andrei Vladimirescu ◽  
...  
Keyword(s):  
Low Power ◽  
Mobile Applications ◽  
High Performance ◽  
Fully Depleted ◽  
Soi Technology

High-Performance Rotation Invariant Multiview Face Detection

2007 ◽  
Vol 29 (4) ◽  
pp. 671-686 ◽  
Author(s):  
Chang Huang ◽  
Haizhou Ai ◽  
Yuan Li ◽  
Shihong Lao
Keyword(s):  
Face Detection ◽  
High Performance ◽  
Rotation Invariant
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