Kazuhiro Hotta

Scale invariant face detection method using higher-order local autocorrelation features extracted from log-polar image

This paper proposes a scale invariant face detection method which combines higher-order local autocorrelation (HLAC) features extracted from a log-polar transformed image with linear discriminant analysis for face and not face classification. Since HLAC features of log-polar images are sensitive to shifts of a face, we utilize this property and develop a face detection method. HLAC features extracted from a log-polar image become scale and rotation invariant because scalings and rotations of a face are expressed as shifts in a log-polar image (coordinate). By combining these features with the linear discriminant analysis which is extended to treat face and not face classes, a scale invariant face detection system can be realized.

Scale and Rotation Invariant Recognition Method Using Higher-Order Local Autocorrelation Features of Log-Polar Image

This paper proposes a scale and rotation invariant recognition method which uses higher-order local autocorrelation (HLAC) features of log-polar image. Linear scalings and rotations are represented as shifts in the log-polar image which is obtained by re-sampling of the input image. HLAC features of log-polar image become robust to the linear scalings and rotations of a target because HLAC features are shift invariant. By combining these features with a simple classifier which uses linear discriminant analysis, we can design a scale and rotation invariant recognition system. Robustness to the scalings and rotations are confirmed by experiments on 2D shapes and face recognition. Robustness to the changes of backgrounds is also confirmed by experiments on face recognition.

Face matching through information theoretical attention points and its applications to face detection and classification

This paper presents a face matching method through information theoretical attention points. The attention points are selected as the points where the outputs of Gabor filters applied to the contrast-filtered image (Gabor features) have rich information. The information value of Gabor features of the certain point is used as the weight and the weighed sum of the correlations is used as the similarity measure for the matching. To cope with the scale changes of a face, several images with different scales are generated by interpolation from the input image and the best match is searched. By using the attention points given from the information theoretical point of view, the matching becomes robust under various environments. This matching method is applied to face detection of a known person and face classification. The effectiveness of the proposed method is confirmed by experiments using the face images captured over years under the different environments.

Dynamic attention map by Ising model for human face detection

We present a method to narrow down the search space for scale-invariant human face detection, which uses a dynamic attention map implemented by Ising dynamics. Combining the proposed method and the scale-invariant face detection method which is based on both higher-order local autocorrelation (HLAC) features of a log-polar image and linear discriminant analysis for face and not face classification, it is shown that the face region in the image can be detected faster with some experiments.

Scale-invariant face recognition method using spectral features of log-polar image

This paper presents scale invariant face detection and classification methods which use spectral features extracted from Log-Polar image. Scale changes of a face in an image are represented as shift along the vertical axis in Log-Polar image. In order to make them robust to the scale changes of faces, spectral features are extracted from the each row of the Log-Polar image. Autocorrelations, Fourier power spectrum, and PARCOR coefficients are used as spectral features. Then these features are combined with simple classification methods based on the Linear Discriminant Analysis to realize scale invariant face detection and classification. The effectiveness of the proposed face detection method is confirmed by the experiment using the face images which are captured under the different scales, backgrounds, illuminations, and dates. We have also performed the experiments to evaluate the proposed face classification method using 2800 face images with 7 scales under 2 different backgrounds.

Multilevel Ising Search for Human Face Detection

In this paper, a multilevel Ising search method for human face detection is proposed to speed up the search. In order to utilize the information obtained from the previous searched points. Ising model is adopted to represent the candidates of `face positions and is combined with the scale invariant human face detection method. In the face detection, the distance from the mean vector of `face class in discriminant space represents the likelihood of face. By integrating the measured distance into the energy function of Ising model as the external magnetic field, the search space is narrowed down effectively (the candidates of `face are reduced). By incorporating color information of face region in the external magnetic field, the `face candidates can be reduced further. In the multilevel Ising search, face candidates (spins) with different resolutions are represented in a Pyramidal structure and the coarse-to-fine strategy is taken. We demonstrate that the proposed multilevel Ising search method can effectively reduce the search space and can detect human face correctly.

Multilevel dynamic attention map for human face detection

The face recognition, as one of the pattern recognition, includes various essence such as the representation and the extraction of the required features, the classification based on the obtained features and the detecting specified regions etc. Previously, we presented the scale and the rotation invariant face recognition method based on both Higher-Order Local Autocorrelation features of log-polar image and linear discriminant analysis for face and not face classification. In this face recognition method, the searching for the face region was performed randomly or sequentially on the image. Therefore its searching performance was not satisfiable. In this paper, we present a method to narrow down the search space by dynamically using the information obtained at the previous search point through constructing the multilevel dynamic attention map, which is constructed based on the Ising dynamics and the renormalization group method.

2P1-A24 Development of an Automated Microscopic Observation System for Asbestos Qualitative Analysis

Abstract: This paper describes to introduce an automated microscopic observation system for supporting asbestos qualitative analysis work. One of major method for visual asbestos qualitative evaluation method is the dispersion staining method. In a usual visual observation process of it, the operators check the asbestos fibers in the view of the microscope and count the number of the fibrous asbestos fibers. For supporting such works, we are developing an automated microscopic observation system for asbestos qualitative analysis. The system can take the images by mounted microscope and save them to the database automatically. In this paper, we introduce the system concept and the performance by using developed prototype system.