• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.897-904
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• 2005

Face Recognition has been an active research area because it is not difficult to acquire face image data and it is applicable in wide range area in real world. Due to the high dimensionality of a face image space, however, it is not easy to process the face images. In this paper, we propose a method to reduce the dimension of the facial data and extract the features from them. It will be solved using the method which extracts the features from holistic face images. The proposed algorithm consists of two parts. The first is the using of principal component analysis (PCA) to transform three dimensional color facial images to one dimensional gray facial images. The second is integrated linear discriminant analusis (PCA+LDA) to prevent the loss of informations in case of performing separated steps. Integrated LDA is integrated algorithm of PCA for reduction of dimension and LDA for discrimination of facial vectors. First, in case of transformation from color image to gray image, PCA(Principal Component Analysis) is performed to enhance the image contrast to raise the recognition rate. Second, integrated LDA(Linear Discriminant Analysis) combines the two steps, namely PCA for dimensionality reduction and LDA for discrimination. It makes possible to describe concise algorithm expression and to prevent the information loss in separate steps. To validate the proposed method, the algorithm is implemented and tested on well controlled face databases.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.334-334
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• 2013

In the past decade, the infrared detectors based on intersubband transition in quantum dots (QDs) have attracted much attention due to lower dark currents and increased lifetimes, which are in turn due a three-dimensional confinement and a reduction of scattering, respectively. In parallel, focal plane array development for infrared imaging has proceeded from the first to third generations (linear arrays, 2D arrays for staring systems, and large format with enhanced capabilities, respectively). For a step further towards the next generation of FPAs, it is envisioned that a two-dimensional metal hole array (2D-MHA) structures will improve the FPA structure by enhancing the coupling to photodetectors via local field engineering, and will enable wavelength filtering. In regard to the improved performance at certain wavelengths, it is worth pointing out the structural difference between previous 2D-MHA integrated front-illuminated single pixel devices and back-illuminated devices. Apart from the pixel linear dimension, it is a distinct difference that there is a metal cladding (composed of a number of metals for ohmic contact and the read-out integrated circuit hybridization) in the FPA between the heavily doped gallium arsenide used as the contact layer and the ROIC; on the contrary, the front-illuminated single pixel device consists of two heavily doped contact layers separated by the QD-absorber on a semi-infinite GaAs substrate. This paper is focused on analyzing the impact of a two dimensional metal hole array structure integrated to the back-illuminated quantum dots-in-a-well (DWELL) infrared photodetectors. The metal hole array consisting of subwavelength-circular holes penetrating gold layer (2DAu-CHA) provides the enhanced responsivity of DWELL infrared photodetector at certain wavelengths. The performance of 2D-Au-CHA is investigated by calculating the absorption of active layer in the DWELL structure using a finite integration technique. Simulation results show the enhanced electric fields (thereby increasing the absorption in the active layer) resulting from a surface plasmon, a guided mode, and Fabry-Perot resonances. Simulation method accomplished in this paper provides a generalized approach to optimize the design of any type of couplers integrated to infrared photodetectors.

• Progress in Medical Physics
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• v.14 no.4
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• pp.211-217
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• 2003

The Principal component analysis (PCA) is a well-known data analysis method that is useful in linear feature extraction and data compression. The PCA is a linear transformation that applies an orthogonal rotation to the original data, so as to maximize the retained variance. PCA is a classical technique for obtaining an optimal overall mapping of linearly dependent patterns of correlation between variables (e.g. neurons). PCA provides, in the mean-squared error sense, an optimal linear mapping of the signals which are spread across a group of variables. These signals are concentrated into the first few components, while the noise, i.e. variance which is uncorrelated across variables, is sequestered in the remaining components. PCA has been used extensively to resolve temporal patterns in neurophysiological recordings. Because the retinal signal is stochastic process, PCA can be used to identify the retinal spikes. With excised rabbit eye, retina was isolated. A piece of retina was attached with the ganglion cell side to the surface of the microelectrode array (MEA). The MEA consisted of glass plate with 60 substrate integrated and insulated golden connection lanes terminating in an 8${\times}$8 array (spacing 200 $\mu$m, electrode diameter 30 $\mu$m) in the center of the plate. The MEA 60 system was used for the recording of retinal ganglion cell activity. The action potentials of each channel were sorted by off­line analysis tool. Spikes were detected with a threshold criterion and sorted according to their principal component composition. The first (PC1) and second principal component values (PC2) were calculated using all the waveforms of the each channel and all n time points in the waveform, where several clusters could be separated clearly in two dimension. We verified that PCA-based waveform detection was effective as an initial approach for spike sorting method.