• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.103-113
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• 2003

We must enhance a fingerprint image to improve the performance of a fingerprint recognition. Because of this reason, many researches were achieved about the fingerprint image enhancement. Representative method is to use Gabor-Filter among them. However GF has the weakness which a processing hour takes long. In this paper, we proposed Half Gabor Filter (HGF) to enhance the fingerprint image fast in the on-line. The HGF, however, can make calculation much simpler, as well as both minutiae-extraction rate and recognition rate. On the other hand, the fingerprint image to enhance using HGF has almost same with the case effectiveness to apply GF. In this paper, we confirme it mathematically and experimentally.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.293-296
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• 2003

The enhancement of fingerprint image is necessary to improve the performance of fingerprint recognition. The enhancement of fingerprint image with Gabor Filter(GF) is widely used. However GF has the weakness such as long processing time and the sensitivity to ridge frequency. To overcome these weaknesses, we propose a Modified Anisotropic Gaussian Filter(MAGF) which is modified from Anisotropic Filter proposed by S. Greenburg's(SAF). This proposed MAGF can reduce the calculation time of ridge frequency and improve the weakness of sensitivity to ridge frequency. We also explained that MAGF is better than others mathematically and experimentally.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1105-1112
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• 2020

Gait recognition, as a promising biometric, can be used in video-based surveillance and other security systems. However, due to the complexity of leg movement and the difference of external sampling conditions, gait recognition still faces many problems to be addressed. In this paper, an improved convolutional neural network (CNN) based on Gabor filter is therefore proposed to achieve gait recognition. Firstly, a gait feature extraction layer based on Gabor filter is inserted into the traditional CNNs, which is used to extract gait features from gait silhouette images. Then, in the process of gait classification, using the output of CNN as input, we utilize metric learning techniques to calculate distance between two gaits and achieve gait classification by k-nearest neighbors classifiers. Finally, several experiments are conducted on two open-accessed gait datasets and demonstrate that our method reaches state-of-the-art performances in terms of correct recognition rate on the OULP and CASIA-B datasets.

• ETRI Journal
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• v.35 no.6
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• pp.1021-1028
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• 2013

Automatic facial expression recognition is a widely studied problem in computer vision and human-robot interaction. There has been a range of studies for representing facial descriptors for facial expression recognition. Some prominent descriptors were presented in the first facial expression recognition and analysis challenge (FERA2011). In that competition, the Local Gabor Binary Pattern Histogram Sequence descriptor showed the most powerful description capability. In this paper, we introduce hybrid facial representations for facial expression recognition, which have more powerful description capability with lower dimensionality. Our descriptors consist of a block-based descriptor and a pixel-based descriptor. The block-based descriptor represents the micro-orientation and micro-geometric structure information. The pixel-based descriptor represents texture information. We validate our descriptors on two public databases, and the results show that our descriptors perform well with a relatively low dimensionality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4395-4412
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• 2020

The multisource image fusion has become an active topic in the last few years owing to its higher segmentation rate. To enhance the accuracy of multimodal pig-body feature segmentation, a multisource image fusion method was employed. Nevertheless, the conventional multisource image fusion methods can not extract superior contrast and abundant details of fused image. To superior segment shape feature and detect temperature feature, a new multisource image fusion method was presented and entitled as NSST-GF-IPCNN. Firstly, the multisource images were resolved into a range of multiscale and multidirectional subbands by Nonsubsampled Shearlet Transform (NSST). Then, to superior describe fine-scale texture and edge information, even-symmetrical Gabor filter and Improved Pulse Coupled Neural Network (IPCNN) were used to fuse low and high-frequency subbands, respectively. Next, the fused coefficients were reconstructed into a fusion image using inverse NSST. Finally, the shape feature was extracted using automatic threshold algorithm and optimized using morphological operation. Nevertheless, the highest temperature of pig-body was gained in view of segmentation results. Experiments revealed that the presented fusion algorithm was able to realize 2.102-4.066% higher average accuracy rate than the traditional algorithms and also enhanced efficiency.