Yasuyuki Takahashi’s research while affiliated with Tokushima University and other places

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Publications (1)


Feature Point Extraction in Face Image by Neural Network
  • Conference Paper

January 2006

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34 Reads

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6 Citations

Yasuyuki Takahashi

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Minoru Fukumi

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Norio Akamatsu

Conventionally, manual operations that specify positions of feature points such as eyes and nose are needed when morphing is carried out for a face image. In this work, the feature points are therefore extracted by using face area detection and a feature points decision methods to automate positional specification of feature points. As a result, the morphing of a face image can be carried out without manually specifying feature points. Face area detection is achieved by a threshold method using the YIQ color system. Feature points decision method extracts feature points by using a 3 layer perceptron type neural network (back-propagation). The attribute of the feature of eyes is defined to be a value of A in the color system LAB. In the same way, the attribute of feature points of the lip is defined as a value of B in the color system LAB. The extraction experiment of feature points was conducted from 120 face images by using the neural network, and the effectiveness of the present method was verified

Citations (1)


... (a) Image, human face, and facial feature extraction, which are commonly accomplished using neural networks [12], propagation filters [13], support vector machine (SVM) [14], etc.; (b) Point cloud feature extraction, which is commonly accomplished using Gaussian normal clustering [15], multi-scale tensor voting [16], etc.; (c) Line segment, moving object trajectory, and graphic boundary feature point extraction, which are commonly accomplished using a Kalman filter [17], trip frequency and accumulated distance [18], compression algorithms, etc. ...

Reference:

Ship Spatiotemporal Key Feature Point Online Extraction Based on AIS Multi-Sensor Data Using an Improved Sliding Window Algorithm
Feature Point Extraction in Face Image by Neural Network
  • Citing Conference Paper
  • January 2006