A convolutional neural network cascade for face detection

Li, Haoxiang; Lin, Zhe; Shen, Xiaohui; Brandt, Jonathan; Hua, Gang

doi:10.1109/cvpr.2015.7299170

Cited by 1,198 publications

(662 citation statements)

References 16 publications

Supporting

Mentioning

652

Contrasting

Unclassified

Order By: Relevance

“…Convolutional Neural Networks Another category, similar to the previous rigid template-based ones, includes the employment of Convolutional Neural Networks (CNNs) and Deep CNNs (DCNNs) (Osadchy et al 2007;Ranjan et al 2015;Li et al 2015a;Yang et al 2015b). Osadchy et al (2007) use a network with four convolution layers and one fully connected layer that rejects the non-face hypotheses and estimates the pose of the correct face hypothesis.…”

Section: Face Detectionmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

View full text Add to dashboard Cite

Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-theart deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

show abstract

Section: Face Detectionmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Moreover, convolutional neural networks (CNN) are becoming extremely popular in pattern recognition in the last few years. Recently, Li et al [53] built a CNN cascade for fast face detection, and the results obtained from this method are considered in Table 4, as well.…”

Section: Results On Face Detectionmentioning

confidence: 99%

Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy

Pujol

Jimeno-Morenilla

López

2017

Entropy

View full text Add to dashboard Cite

Face detection is the first step of any automated face recognition system. One of the most popular approaches to detect faces in color images is using a skin color segmentation scheme, which in many cases needs a proper representation of color spaces to interpret image information. In this paper, we propose a fuzzy system for detecting skin in color images, so that each color tone is assumed to be a fuzzy set. The Red, Green, and Blue (RGB), the Hue, Saturation and Value (HSV), and the YCbCr (where Y is the luminance and Cb,Cr are the chroma components) color systems are used for the development of our fuzzy design. Thus, a fuzzy three-partition entropy approach is used to calculate all of the parameters needed for the fuzzy systems, and then, a face detection method is also developed to validate the segmentation results. The results of the experiments show a correct skin detection rate between 94% and 96% for our fuzzy segmentation methods, with a false positive rate of about 0.5% in all cases. Furthermore, the average correct face detection rate is above 93%, and even when working with heterogeneous backgrounds and different light conditions, it achieves almost 88% correct detections. Thus, our method leads to accurate face detection results with low false positive and false negative rates.

show abstract

“…1. Combined structure of one-dimensional CNN and LSTM maximum of the previous feature maps, which can be expressed as follows [17]:…”

Section: Methodsmentioning

confidence: 99%