Precise multi-level face detector for advanced analysis of facial images

Kawulok, Michał; Szymanek, Janusz

doi:10.1049/iet-ipr.2010.0495

Cited by 40 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, the match scores generated by the face, fingerprint and hand modalities of a user combined via the simple sum rule to obtain a new match score, after that it is used to make the final decision [18]. A rest of some previous work in [15,18,26,33,34,[42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57], considers matching score level fusion.…”

Section: ) Matching Score Level Fusionmentioning

confidence: 99%

Multi-Biometric Systems: A State of the Art Survey and Research Directions

Gad¹,

El-Fishawy²,

El‐Sayed³

et al. 2015

ijacsa

View full text Add to dashboard Cite

Abstract-Multi-biometrics is an exciting and interesting research topic. It is used to recognizing individuals for security purposes; to increase security levels. The recent research trends toward next biometrics generation in real-time applications. Also, integration of biometrics solves some of unimodal system limitations. However, design and evaluation of such systems raises many issues and trade-offs. A state of the art survey of multi-biometrics benefits, limitations, integration strategies, and fusion levels are discussed in this paper. Finally, upon reviewing multi-biometrics approaches and techniques; some open points are suggested to be considered as a future research point of interest.

show abstract

Section: ) Matching Score Level Fusionmentioning

confidence: 99%

Multi-Biometric Systems: A State of the Art Survey and Research Directions

Gad¹,

El-Fishawy²,

El‐Sayed³

et al. 2015

ijacsa

View full text Add to dashboard Cite

show abstract

“…The directional image is a set of oriented segments, and it was used in our earlier work for face detection [24].…”

Section: Directional Imagementioning

confidence: 99%

Hand landmarks detection and localization in color images

Grzejszczak

Kawulok

Gałuszka

2015

Multimed Tools Appl

View full text Add to dashboard Cite

This paper introduces a new method for detecting and localizing hand landmarks in 2D color images. Location of the hand landmarks is an important source of information for recognizing hand gestures, effectively exploited in a number of recent methods which operate from the depth maps. However, this problem has not yet been satisfactorily solved for 2D color images. Here, we propose to analyze the skin-presence masks, as well as the directional image of a hand using the distance transform and template matching. This makes it possible to detect the landmarks located both at the contour and inside the hand masks. Moreover, we performed an extensive experimental study to compare the proposed method with a number of state-of-the-art algorithms. The obtained quantitative and qualitative results clearly indicate that our approach outperforms other methods, which may help improve the existing gesture recognition systems.

show abstract

“…The remaining 2,000 images from the ECU set and all of the images from the HGR set were used as the test set. The test set consists of the images, in which the faces were detected with the method described in [66], so that our method can be compared with face-based adaptation schemes. The lists of images used for training and testing are available in Additional file 1.…”

Section: Experimental Validationmentioning

confidence: 99%

Self-adaptive algorithm for segmenting skin regions

Kawulok

Nalepa

et al. 2014

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

In this paper, we introduce a new self-adaptive algorithm for segmenting human skin regions in color images. Skin detection and segmentation is an active research topic, and many solutions have been proposed so far, especially concerning skin tone modeling in various color spaces. Such models are used for pixel-based classification, but its accuracy is limited due to high variance and low specificity of human skin color. In many works, skin model adaptation and spatial analysis were reported to improve the final segmentation outcome; however, little attention has been paid so far to the possibilities of combining these two improvement directions. Our contribution lies in learning a local skin color model on the fly, which is subsequently applied to the image to determine the seeds for the spatial analysis. Furthermore, we also take advantage of textural features for computing local propagation costs that are used in the distance transform. The results of an extensive experimental study confirmed that the new method is highly competitive, especially for extracting the hand regions in color images.

show abstract

Precise multi-level face detector for advanced analysis of facial images

Cited by 40 publications

References 26 publications

Multi-Biometric Systems: A State of the Art Survey and Research Directions

Multi-Biometric Systems: A State of the Art Survey and Research Directions

Hand landmarks detection and localization in color images

Self-adaptive algorithm for segmenting skin regions

Contact Info

Product

Resources

About