Fast Localization of Facial Landmark Points

Markuš, Nenad; Frljak, Miroslav; Pandžić, Igor S.; Ahlberg, Jörgen; Forchheimer, Robert

doi:10.20532/ccvw.2014.0001

Cited by 12 publications

(10 citation statements)

References 13 publications

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“…To validate the strength of the proposed new even system further, we validate it on the widely used BioID dataset [56], where it obtains a 100% detection rate with a limited number of false positives. Our best ensemble/filter combination outperforms the method proposed by Markuš et al [34], which has been shown to surpass the performance of these well-known state-of-the-art commercial face detection systems: Google Picasa, Face++, and Intel Olaworks.…”

Section: Introductionmentioning

confidence: 74%

“…We perform experiments on the fusion of six face detectors: the four detectors tested in [9] (the canonic VJ algorithm [14], a method using the Split up sparse Network of Winnows (SN) classifier [31], a modification of the VJ algorithm with fast localization (FL) [34], and a face detector based on Discriminative Response Map Fitting (DRMF) [32]), as well as two additional face detectors (the VJ modification using NPD features (NPD) [33] and a high-performance method implemented here: . In the following, this latter method is called Single Scale-invariant Face Detector (SFD).…”

Section: Methodsmentioning

confidence: 99%

“…Four 2D models of interest in this study are the face detectors proposed by Nilsson et al [31], Asthana et al [32], Liao et al [33], and Markuš et al [34]. Nilsson et al [31] used successive mean quantization transform (SMQT) features that they applied to a Split up sparse Network of Winnows (SN) classifier.…”

Section: Introductionmentioning

confidence: 99%

“…Asthana et al [32] employed face fitting, i.e., a method that models a face shape with a set of parameters for controlling a facial deformable model. Markuš et al [34] combined a modified VJ method with an algorithm for localizing salient facial landmark points. Liao et al [33], in addition to proposing the aforementioned scale-invariant NPD features, expanded the original VJ tree classifier with two leaves to a deeper quadratic tree structure.…”

Section: Introductionmentioning

confidence: 99%

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Face Detection Ensemble with Methods Using Depth Information to Filter False Positives

Nanni

Brahnam

Lumini

2019

Sensors

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A fundamental problem in computer vision is face detection. In this paper, an experimentally derived ensemble made by a set of six face detectors is presented that maximizes the number of true positives while simultaneously reducing the number of false positives produced by the ensemble. False positives are removed using different filtering steps based primarily on the characteristics of the depth map related to the subwindows of the whole image that contain candidate faces. A new filtering approach based on processing the image with different wavelets is also proposed here. The experimental results show that the applied filtering steps used in our best ensemble reduce the number of false positives without decreasing the detection rate. This finding is validated on a combined dataset composed of four others for a total of 549 images, including 614 upright frontal faces acquired in unconstrained environments. The dataset provides both 2D and depth data. For further validation, the proposed ensemble is tested on the well-known BioID benchmark dataset, where it obtains a 100% detection rate with an acceptable number of false positives.

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Section: Introductionmentioning

confidence: 74%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Face Detection Ensemble with Methods Using Depth Information to Filter False Positives

Nanni

Brahnam

Lumini

2019

Sensors

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show abstract

“…(10) Jurić and Lončarić (11) adopted the method proposed in Ref. 12, which did not require manual parameter adjustments and boasted simpler processes that reduced the amount of computation needed, but the results of that study indicated a low vehicle correction rate. In Ref.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent IoT-based Vision System for Nighttime Vehicle Detection and Energy Saving

Hsia¹,

Yen²,

Jang³

2019

Sensors and Materials

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In recent years, the rise of industrial societies and the Internet of Things (IoT) has encouraged the growth of vehicular transportation, which, in turn, has led to intelligent transportation systems (ITSs) becoming an important field of research. In view of this, nighttime vehicle detection and the counting technique that will facilitate future research on energy saving are presented. In this work, the Hough transform was first performed to detect lane lines in an image, and subsequently, all images underwent feature enhancement processing. Next, the light source data from the detected line of each lane was obtained, and at the same time, the aspect ratio and spacing of light source pairs were computed in order to determine if they matched the set values and to perform vehicle counting. Furthermore, since driving habits differ and some vehicles would straddle the lane line, an approach to recognizing and counting lane-straddling vehicles in order to avoid misjudgments is proposed in this study. Lastly, LED lights were used to simulate street lights and controlled on the basis of the traffic volume data obtained in the manner described earlier. The experimental results show that the proposed technique could be effectively utilized to perform nighttime vehicle detection and counting since it achieved a high average correction rate of 94%, as well as a computing time of 44 frames per second.

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Face Detection Coupling Texture, Color and Depth Data

Nanni

Lumini

Minto

et al. 2016

Advances in Face Detection and Facial Image Analysis

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In this chapter, we propose an ensemble of face detectors for maximizing the number of true positives found by the system. Unfortunately, combining different face detectors increases both the number of true positives and false positives. To overcome this difficulty, several methods for reducing false positives are tested and proposed. The different filtering steps are based on the characteristics of the depth map related to the subwindows of the whole image that contain the candidate faces. The most simple and easiest criteria to use, for instance, is to filter the candidate face region by considering its size in metric units.\ud \ud The experimental section demonstrates that the proposed set of filtering steps greatly reduces the number of false positives without decreasing the detection rate. The proposed approach has been validated on a dataset of 549 images (each including both 2D and depth data) representing 614 upright frontal faces. The images were acquired both outdoors and indoors, with both first and second generation Kinect sensors. This was done in order to simulate a real application scenario. Moreover, for further validation and comparison with the state-of-the-art, our ensemble of face detectors is tested on the widely used BioID dataset where it obtains 100 % detection rate with an acceptable number of false positives.\ud \ud A MATLAB version of the filtering steps and the dataset used in this paper will be freely available from http://www.dei.unipd.it/node/2357

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Fast Localization of Facial Landmark Points

Cited by 12 publications

References 13 publications

Face Detection Ensemble with Methods Using Depth Information to Filter False Positives

Face Detection Ensemble with Methods Using Depth Information to Filter False Positives

An Intelligent IoT-based Vision System for Nighttime Vehicle Detection and Energy Saving

Face Detection Coupling Texture, Color and Depth Data

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