Learning SURF Cascade for Fast and Accurate Object Detection

Li, Jianguo; Zhang, Yimin

doi:10.1109/cvpr.2013.445

Cited by 189 publications

(113 citation statements)

References 32 publications

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“…Characteristic examples of other methods that employ variations of AdaBoost include Li et al (2002), Wu et al (2004), Mita et al (2005). The original VJ algorithm used Haar features, however boosting (or cascade of classifiers methodologies in general) have been shown to greatly benefit from robust features (Köstinger et al 2012;Jun et al 2013;Li et al 2011;Li and Zhang 2013;Mathias et al 2014;Yang et al 2014a), such as HOG (Dalal and Triggs 2005), SIFT (Lowe 1999), SURF (Bay et al 2008) and LBP (Ojala et al 2002). For example, SURF features have been successfully combined with a cascade of weak classifiers in Li et al (2011), Li and, achieving faster convergence.…”

Section: Face Detectionmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Face Detectionmentioning

confidence: 99%

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

et al. 2017

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“…Logistic regression was adopted as the weak classifier on each local SURF patch and Area Under ROC curve (AUC) was used as the criterion for convergence. In [103] it was shown that the SURF cascade is able to converge very fast (within even an hour on a standard desktop).…”

Section: Robust Descriptors Meet Boostingmentioning

confidence: 99%

“…One of the first such approaches was recently introduced combining a cascade of weak-classifiers with SURF features [102,103]. In particular, in [103] the detection region was represented by patches and each patch was described by a multi-dimensional SURF descriptor.…”

Section: Robust Descriptors Meet Boostingmentioning

confidence: 99%

“…In particular, in [103] the detection region was represented by patches and each patch was described by a multi-dimensional SURF descriptor. The number of SURF patches contained only few hundreds of features.…”

Section: Robust Descriptors Meet Boostingmentioning

confidence: 99%

“…A number of follow-up works showed that there is indeed information in the results from the previous nodes, and it is best to reuse them [80,19] etc. Spectral histogram [86] Spatial histogram (LBP-based) [87] HoG and LBP [88] Region covariance [89] SURF [102,103] Composite Joint Haar-like features [62] features Sparse feature set [90] LGB, BHOG [22] Integral Channel Features on HoG and LUV (Headhunter) [26] HoG, HSV, RGB, LUV, Grayscale, Gradient Magnitude [105] Shape features Boundary/contour fragments [94,95] Edgelet [96] Shapelet [97] instead of starting from scratch at each new node. For instance, in [110], the use of a "chain" structure was proposed to integrate historical knowledge into successive boosting learning.…”

Section: Variations Of the Boosting Learning Algorithmmentioning

confidence: 99%

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A survey on face detection in the wild: Past, present and future

Zafeiriou

Cha

Zhang

2015

Computer Vision and Image Understanding

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Multi‐view face detection using frontal face detector

You

Akashi

2018

IEEJ Transactions Elec Engng

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The development of frontal face detection has matured for application to everyday life. However, various profile face views are still difficult to deal with practically. A common solution is to build different detectors organized by a decision tree and ensure that each detector handles a single view or a few views. However, collecting the training images for each face view is time consuming and laborious. Moreover, many profile face detectors cannot perform as well as the frontal face detector because of insufficient training images. The management of many detectors also leads to too complex a logic structure to classify a face into its corresponding view. In this paper,we propose a novel method to reuse a frontal face detector to detect multi‐view faces, which do not need any data collection or training processes for various profile views. We focus on exploiting the potential of a general frontal face detector and aim to extend its application to multi‐view faces. We conduct a theoretical analysis to explain why our methodology works from different perspectives, and implement the proposed method based on the sliding window strategy. Furthermore, the searching process is optimized by a genetic algorithm with an original fitness function. Experimental results verify that our method can successfully detect human faces in almost all head poses in the dataset containing a complete collection of head poses in yaw and pitch axes. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Learning SURF Cascade for Fast and Accurate Object Detection

Abstract: Abstract

Cited by 189 publications

References 32 publications

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

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

A survey on face detection in the wild: Past, present and future

Multi‐view face detection using frontal face detector

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