Face detection with the modified census transform

Fröba, Bernhard; Ernst, Andreas T.

doi:10.1109/afgr.2004.1301514

Cited by 382 publications

(257 citation statements)

References 6 publications

Supporting

Mentioning

255

Contrasting

Unclassified

Order By: Relevance

“…The key properties of the census transform are of course not restricted to optic flow models. They have already proven to be equally beneficial for other computer vision tasks such as stereo reconstruction [6] or face detection [19].…”

Section: Discussionmentioning

confidence: 99%

Why Is the Census Transform Good for Robust Optic Flow Computation?

Hafner

Demetz

Weickert

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. The census transform is becoming increasingly popular in the context of optic flow computation in image sequences. Since it is invariant under monotonically increasing grey value transformations, it forms the basis of an illumination-robust constancy assumption. However, its underlying mathematical concepts have not been studied so far. The goal of our paper is to provide this missing theoretical foundation. We study the continuous limit of the inherently discrete census transform and embed it into a variational setting. Our analysis shows two surprising results: The census-based technique enforces matchings of extrema, and it induces an anisotropy in the data term by acting along level lines. Last but not least, we establish links to the widely-used gradient constancy assumption and present experiments that confirm our findings.

show abstract

Section: Discussionmentioning

confidence: 99%

Why Is the Census Transform Good for Robust Optic Flow Computation?

Hafner

Demetz

Weickert

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…In [69] a modified census transform was adopted to generate illumination-insensitive features for face detection. On each pixel's 3 × 3 neighborhood, the method applied a modified census transform that compares the neighborhood pixels with their intensity mean.…”

Section: Feature Extractionmentioning

confidence: 99%

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

Zafeiriou

Cha

Zhang

2015

Computer Vision and Image Understanding

386

167

View full text Add to dashboard Cite

“…Since each HLBP feature is linked with exactly one LBP label, there is no need to consider the entire LBP histogram in training and test, as in [4]. Thus our system is more efficient in terms of storage requirements as well as speed (ref.…”

mentioning

confidence: 99%

“…The first is the baseline system based on Haar features [20]. The second is the system based on MCT [4] which is one of the best performing systems representing the state of the art today. 1 The rest of the paper is organized as follows: we first introduce the proposed HLBP features in Sec.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Haar Local Binary Pattern Feature for Fast Illumination Invariant Face Detection

Marcel¹

2009

Procedings of the British Machine Vision Conference 2009

View full text Add to dashboard Cite

Face detection is the first step in many visual processing systems like face recognition, emotion recognition and lip reading. In this paper, we propose a novel feature called Haar Local Binary Pattern (HLBP) feature for fast and reliable face detection, particularly in adverse imaging conditions. This binary feature compares bin values of Local Binary Pattern histograms calculated over two adjacent image subregions. These subregions are similar to those in the Haar masks, hence the name of the feature. They capture the region-specific variations of local texture patterns and are boosted using AdaBoost in a framework similar to that proposed by Viola and Jones. Preliminary results obtained on several standard databases show that it competes well with other face detection systems, especially in adverse illumination conditions.

show abstract

Face detection with the modified census transform

Abstract: Illumination variation is a big problem in object recogni

Cited by 382 publications

References 6 publications

Why Is the Census Transform Good for Robust Optic Flow Computation?

Why Is the Census Transform Good for Robust Optic Flow Computation?

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

Haar Local Binary Pattern Feature for Fast Illumination Invariant Face Detection

Contact Info

Product

Resources

About