Abstract: A facial image-processing method includes: transforming a facial image with 2D Fourier transformation (FT) in a template to obtain 2D FT data of color channels of the facial image and a 2D FT data of the template, with computing first light intensities of color channels and a second light intensity of the template with the 2D FT data; computing an intensity mean value and an intensity maximum in each of the color channels; processing the first light intensities and the second light intensity with singular value decomposition (SVD) to obtain intensity spectrum SVD matrixes and a template SVD matrix; computing a compensation weight coefficient for each color channel with the intensity mean value, the intensity maximum and SV maximums of the intensity spectrum SVD matrixes and the template SVD matrix; and compensating the facial image with the compensation weight coefficients to obtain a compensated facial image.

Abstract: A 3D hand gesture recognition system includes a light field capturing unit, a calculation unit and an output unit. The light field capturing unit is provided to capture a hand gesture action to obtain a 3D hand gesture image. The calculation unit connects with the light field capturing unit and is provided to project the 3D hand gesture image to a predetermined space to obtain eigenvectors which are compared with the samples to classify and recognize a signal of the 3D hand gesture image. The output unit connects with the calculation unit to output the signal of the 3D hand gesture image to a predetermined device.

Abstract: A facial image-processing method includes: transforming a facial image with 2D Fourier transformation (FT) in a template to obtain 2D FT data of color channels of the facial image and a 2D FT data of the template, with computing first light intensities of color channels and a second light intensity of the template with the 2D FT data; computing an intensity mean value and an intensity maximum in each of the color channels; processing the first light intensities and the second light intensity with singular value decomposition (SVD) to obtain intensity spectrum SVD matrixes and a template SVD matrix; computing a compensation weight coefficient for each color channel with the intensity mean value, the intensity maximum and SV maximums of the intensity spectrum SVD matrixes and the template SVD matrix; and compensating the facial image with the compensation weight coefficients to obtain a compensated facial image.

Abstract: A fingerprint processing system includes an input unit, a calculation unit and an output unit. The input unit is applied to input an original fingerprint image. The calculation unit is applied to decompose the original fingerprint image to a decomposed image by singular value decomposition (SVD) and the decomposed image is transformed into a plurality of sub-band images by discrete wavelet transformation (DWT) with a template. A plurality of compensation weight coefficients of DWT are calculated to compensate the sub-band images to generate a plurality of compensated sub-band images which are rebuilt by an inverse DWT. After rebuilding the compensated sub-band images, the output unit is applied to output an enhanced fingerprint image.

Abstract: A fingerprint processing system includes an input unit, a calculation unit and an output unit. The input unit is applied to input an original fingerprint image. The calculation unit is applied to decompose the original fingerprint image to a decomposed image by singular value decomposition (SVD) and the decomposed image is transformed into a plurality of sub-band images by discrete wavelet transformation (DWT) with a template. A plurality of compensation weight coefficients of DWT are calculated to compensate the sub-band images to generate a plurality of compensated sub-band images which are rebuilt by an inverse DWT. After rebuilding the compensated sub-band images, the output unit is applied to output an enhanced fingerprint image.

Abstract: A recognition method includes: retrieving a component image from an image source; utilizing SVD (singular value decomposition) to decompose the component image; and utilizing the decomposed component image for image recognition. In an embodiment, the recognition method further includes: utilizing a light compensation method to calculate at least one light compensation coefficient; utilizing the light compensation coefficient to process the component image to obtain a light-compensated component image; and utilizing the light-compensated component image for image recognition.

Abstract: A method for predicting constellations includes the steps of: providing probability data of correlations between constellations and combined fingerprint types and blood types, and constellation fortune-telling data; detecting and classifying a fingerprint image; inputting a personal blood type; calculating probabilities of correlations between the constellations and the combined fingerprint types and blood types; and correspondingly outputting the constellation fortune-telling data according to the calculated probability. A constellation-predicting system of a preferred embodiment includes a fingerprint-image-detecting device, a blood-type-inputting unit, a calculating unit, a memory unit and a terminal unit which are connected to form the system.

Abstract: A recognition method includes: retrieving a component image from an image source; utilizing SVD (singular value decomposition) to decompose the component image; and utilizing the decomposed component image for image recognition. In an embodiment, the recognition method further includes: utilizing a light compensation method to calculate at least one light compensation coefficient; utilizing the light compensation coefficient to process the component image to obtain a light-compensated component image; utilizing the light-compensated component image for image recognition.

Abstract: A method for predicting constellations comprises the steps of: providing probability data of correlations between constellations and combined fingerprint types and blood types, and constellation fortune-telling data; detecting and classifying a fingerprint image; inputting a personal blood type; calculating probabilities of correlations between the constellations and the combined fingerprint types and blood types, and correspondingly outputting the constellation fortune-telling data according to the calculated probability. A constellation-predicting system of a preferred embodiment includes a fingerprint-image-detecting device, a blood-type-inputting unit, a calculating unit, a memory unit and a terminal unit which are connected to form the system.