Face detection based on multi-scale enhanced local texture feature sets

2012 ◽  
Author(s):  
Zhe Wei ◽  
Yuan Dong ◽  
Feng Zhao ◽  
Hongliang Bai
Keyword(s):  
Face Detection ◽  
Texture Feature ◽  
Feature Sets ◽  
Multi Scale ◽  
Local Texture

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.

An improved multi-scale face detection using convolutional neural network

2020 ◽  
Vol 14 (7) ◽  
pp. 1345-1353 ◽  
Author(s):  
Hazar Mliki ◽  
Sahar Dammak ◽  
Emna Fendri
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Multi Scale

scholarly journals Automated Facial Expression Recognition Using Gradient-Based Ternary Texture Patterns

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Faisal Ahmed ◽  
Emam Hossain
Keyword(s):  
Facial Expression ◽  
Recognition Performance ◽  
Texture Feature ◽  
Recognition Task ◽  
Facial Feature ◽  
Recognition System ◽  
Feature Descriptor ◽  
Expression Recognition ◽  
Local Texture ◽  
Face Expression

Recognition of human expression from facial image is an interesting research area, which has received increasing attention in the recent years. A robust and effective facial feature descriptor is the key to designing a successful expression recognition system. Although much progress has been made, deriving a face feature descriptor that can perform consistently under changing environment is still a difficult and challenging task. In this paper, we present the gradient local ternary pattern (GLTP)—a discriminative local texture feature for representing facial expression. The proposed GLTP operator encodes the local texture of an image by computing the gradient magnitudes of the local neighborhood and quantizing those values in three discrimination levels. The location and occurrence information of the resulting micropatterns is then used as the face feature descriptor. The performance of the proposed method has been evaluated for the person-independent face expression recognition task. Experiments with prototypic expression images from the Cohn-Kanade (CK) face expression database validate that the GLTP feature descriptor can effectively encode the facial texture and thus achieves improved recognition performance than some well-known appearance-based facial features.

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