Face detection with boosted Gaussian features

Meynet, Julien; Popovici, Vlad; Thiran, Jean-Philippe

doi:10.1016/j.patcog.2007.02.001

Cited by 41 publications

(13 citation statements)

References 16 publications

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“…Other more recently reported methods [25] are often variations and/or extensions of [21], using Haar-like features or local binary patterns (LBP) or anisotropic Gaussian features filters with AdaBoost type algorithms ( [2], [8], [10], [11], [12], [13], [20], [24]), or employ other techniques like support vector machine, SVM ( [2], [6], [7], [23]), Haar wavelets ( [17]), convolutional neural networks -CNN / ConvNet ( [3]), facial landmarks models ( [26]), or energy based methods ( [14]), while (some) are still using portions of image scanning and preprocessing as in [19] and/or [16]. These methods also demonstrate good (or promising) results, several not only for the frontal-view, but also for the multi-view case.…”

Section: Parallels With Other Methodsmentioning

confidence: 99%

A Pattern Matching Method and Algorithm for Face Detection

Vrejoiu¹

2017

SIC

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Abstract:The paper presents a simple, original method, using supervised learning and pattern matching, for frontal-view face detection. The raw method and algorithms, their refinements and optimizations, the experimental system, and the obtained results are described incrementally. A pyramid of several simplified representations for faces, with gradual complexity and dimensionality from coarser to more detailed ones, has been defined. These representations were used as a basis for structuring and organizing the knowledge base as a kind of hash tree, as well as to optimize the developed algorithms and their processing time, by applying a sequence of filters with gradual computational complexity in cascade at detection. Appropriate metrics have been defined and used to evaluate the similarity between two finite sets of binary values, of the same dimension, with a minimum of computational effort in these filters. Various thresholds of passage were empirically chosen and adjusted. An experimental system was developed and used for learning and detection tests. Public image databases and private images have been employed, and quite promising results have been obtained. Finally, comparative parallels with some reference methods are discussed.

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Section: Parallels With Other Methodsmentioning

confidence: 99%

A Pattern Matching Method and Algorithm for Face Detection

Vrejoiu¹

2017

SIC

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“…Note the Haar-like feature set is a subset of linear features. Another example is the anisotropic Gaussian filters in [75]. In [76], the linear features were constructed by pre-learning them using local non-negative matrix factorization (LNMF), which is still sub-optimal.…”

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

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“…The machine learning algorithm is preferred for better face features. It can be categorized based on appearance and based on boosting [26]. In appearance method the classification stage need not be consider.…”

Section: Face Detectionmentioning

confidence: 99%

Boosting Based Implementationof Biometric Authentication in IoT

Thilagavathi

Suthendran

2018

JCSM

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In security and control application the biometric authentication played a specific and important role to identify the person. Analysis of face recognization is the prerequisite process for the entire authentication. This paper focuses an automatic real-time implementation of face recognization system by highlevel description language such as python. Comparing the biometrics where still images are used, video based biometric holds ample information than a single image. It provides the innovative solution for automatic real time face recognition from the video by the following algorithms like Adaboost, Haar cascade classifier and local binary pattern Histogram (LBPH). From the video stream, the input images are trained by adaboost algorithm which is implemented in cascade classifier. AdaBoost is called as adaptive boosting algorithm. It is a learning algorithm which has been combined with a weak classifier in order to form a strong classifier. Here, the real time testing image is compared with the 627 frames of trained images which are obtained from 47.7 MB (56088648 bytes) video. The hardware implementation of face recognization is obtained and the result can be stored and viewed in http://169.254.108.24. Thus by the above said procedures are authenticated by the combinational performance of Adaboost and Cascade classifier.

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Face detection with boosted Gaussian features

Cited by 41 publications

References 16 publications

A Pattern Matching Method and Algorithm for Face Detection

A Pattern Matching Method and Algorithm for Face Detection

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

Boosting Based Implementationof Biometric Authentication in IoT

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