Head Pose Estimation on Low Resolution Images

Gourier, Nicolas; Maisonnasse, Jérôme; Hall, Daniela; Crowley, James L.

doi:10.1007/978-3-540-69568-4_24

Cited by 87 publications

(53 citation statements)

References 12 publications

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“…The methods used for determining head pose may be categorized as local or global ( [11], [7]). Global methods use template matching technique while local methods use a set of facial features like eyes, eyebrows and lips to estimate the head-pose.…”

Section: Head-pose Estimationmentioning

confidence: 99%

A Novel Face Features Extraction and Neural Network Based Face Quality Assessment Technique

Mutneja¹,

Singh²

2017

IJSIP

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Section: Head-pose Estimationmentioning

confidence: 99%

A Novel Face Features Extraction and Neural Network Based Face Quality Assessment Technique

Mutneja¹,

Singh²

2017

IJSIP

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“…In [23], the authors show that the multi-scale Gaussian derivatives, which are a particular case of steerable filters, combined to SVM give good results. In [26], normalized faces are used to train an auto-associative memory using the Widrow-Hoff correction rule in order to classify head poses. In one of the most recent works [27], the authors consider that object detection and continuous pose estimation are interdependent problems and they jointly formulate them as a structured prediction problem, by learning a single and continuously parameterized object appearance model over the entire pose space.…”

Section: Appearance-based Head Pose Estimationmentioning

confidence: 99%

Driver head pose estimation using efficient descriptor fusion

Alioua

Amine

Rogozan

et al. 2016

J Image Video Proc.

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A great interest is focused on driver assistance systems using the head pose as an indicator of the visual focus of attention and the mental state. In fact, the head pose estimation is a technique allowing to deduce head orientation relatively to a view of camera and could be performed by model-based or appearance-based approaches. Modelbased approaches use a face geometrical model usually obtained from facial features, whereas appearance-based techniques use the whole face image characterized by a descriptor and generally consider the pose estimation as a classification problem. Appearance-based methods are faster and more adapted to discrete pose estimation. However, their performance depends strongly on the head descriptor, which should be well chosen in order to reduce the information about identity and lighting contained in the face appearance. In this paper, we propose an appearancebased discrete head pose estimation aiming to determine the driver attention level from monocular visible spectrum images, even if the facial features are not visible. Explicitly, we first propose a novel descriptor resulting from the fusion of four most relevant orientation-based head descriptors, namely the steerable filters, the histogram of oriented gradients (HOG), the Haar features, and an adapted version of speeded up robust feature (SURF) descriptor. Second, in order to derive a compact, relevant, and consistent subset of descriptor's features, a comparative study is conducted on some well-known feature selection algorithms. Finally, the obtained subset is subject to the classification process, performed by the support vector machine (SVM), to learn head pose variations. As we show in experiments with the public database (Pointing'04) as well as with our real-world sequence, our approach describes the head with a high accuracy and provides robust estimation of the head pose, compared to state-of-the-art methods.

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“…[4][5] Recognition algorithms can be divided into two main approaches, first is geometric, which look at distinguishing features, and second is photometric, which is a statistical approach that refine an image into values and compares the values with templates in order to eliminate variances. [11] Basically for evaluation of various face poses different techniques are used. But feature extraction method is one of…”

Section: Introductionmentioning

confidence: 99%

Human Face Pose Estimation based on Feature Extraction Points

Bhullar¹,

Mutneja²

2016

IJCA

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The process of Face Recognition comprises of Face Detection, feature extraction and verification or identification. The extraction and identification are stages in the FR process. Many face recognition algorithms have been developed. This has resulted in development of manifold robust techniques such as background removal, illumination normalization and others which support the algorithm to withstand the undesirable effects and improve the success rate. This paper provides a survey and method for face pose estimation. This method is based on feature extraction points of two different face poses and then matched points between these two face poses will give the results. This method is one of the simplest methods for low resolution images.

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Head Pose Estimation on Low Resolution Images

Cited by 87 publications

References 12 publications

A Novel Face Features Extraction and Neural Network Based Face Quality Assessment Technique

A Novel Face Features Extraction and Neural Network Based Face Quality Assessment Technique

Driver head pose estimation using efficient descriptor fusion

Human Face Pose Estimation based on Feature Extraction Points

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