Classification of Face Images for Gender, Age, Facial Expression, and Identity

Wilhelm, Torsten; Böhme, Hans-Joachim; Groß, Horst–Michael

doi:10.1007/11550822_89

Cited by 20 publications

(15 citation statements)

References 6 publications

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“…Wide spread are on the one hand direct statistical analysis such as and generative models for faces. A comparison of ICA and one of the most prominent generative approaches -the active appearance models (AAM) -reports a slightly better performance in face recognition for the first [19]. However, the differences are minor and ICA demands for precisely aligned images and is considered to be less robust in real world settings.…”

Section: B Feature Extractionmentioning

confidence: 99%

Who am I talking with? A face memory for social robots

Hanheide

Wrede

Lang

et al. 2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-In order to provide personalized services and to develop human-like interaction capabilities robots need to recognize their human partner. Face recognition has been studied in the past decade exhaustively in the context of security systems and with significant progress on huge datasets. However, these capabilities are not in focus when it comes to social interaction situations. Humans are able to remember people seen for a short moment in time and apply this knowledge directly in their engagement in conversation. In order to equip a robot with capabilities to recall human interlocutors and to provide useraware services, we adopt human-human interaction schemes to propose a face memory on the basis of active appearance models integrated with the active memory architecture. This paper presents the concept of the interactive face memory, the applied recognition algorithms, and their embedding into the robot's system architecture. Performance measures are discussed for general face databases as well as scenario-specific datasets.

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Section: B Feature Extractionmentioning

confidence: 99%

Who am I talking with? A face memory for social robots

Hanheide

Wrede

Lang

et al. 2008

2008 IEEE International Conference on Robotics and Automation

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“…When the background is close to the skin colour, movement across successive frames is tested to confirm the presence of a human face. Facial features play an important role in identifying age, gender and emotion information (46). Human emotion can be estimated using eyes, lips and their measures (gradient, distance of eyelids or lips).…”

Section: High Level Features Extraction From Videomentioning

confidence: 99%

Generating natural language tags for video information management

Khan

Gotoh

2017

Machine Vision and Applications

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This exploratory work is concerned with generation of natural language descriptions that can be used for video retrieval applications. It is a step ahead of keyword based tagging as it captures relations between keywords associated with videos. Firstly we prepare hand annotations consisting of descriptions for video segments crafted from a TREC Video dataset. Analysis of this data presents insights into human's interests on video contents. Secondly we develop a framework for creating smooth and coherent description of video streams. It builds on conventional image processing techniques that extract high level features from individual video frames. Natural language description is then produced based on high level features. Although feature extraction processes are erroneous at various levels, we explore approaches to putting them together to produce a coherent, smooth and well phrased description by incorporating spatial and temporal information. Evaluation is made by calculating ROUGE scores between human annotated and machine generated descriptions. Further we introduce a task based evaluation by human subjects which provides qualitative evaluation of generated descriptions.

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“…Active appearance models (AAM) [21] have also been used as a feature extraction mechanism in gender classification. In [13], the AAM was compared with ICA for gender classification using four different classifiers. In [15], Saatci and Town utilized AAM and support vector machine (SVM) for gender and expression recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

“…An accurate and reliable gender classification approach can also improve the performance of face identity recognition. Therefore face gender classification has attracted significant attention in computer vision [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][39][40][41][42][43][44][45], as well as in psychology [17][18][19][20]. However, although studies [18] have shown that gender is not only revealed by 2D facial texture, but also has a close relationship with the 3D shape of the human face, relatively few studies have investigated the role of 3D shape in gender classification [14].…”

Section: Introductionmentioning

confidence: 99%