Human Identification Based on ECG and Palmprint

Zokaee, Sara; Faez, Karim

doi:10.11591/ijece.v2i2.292

Cited by 27 publications

(32 citation statements)

References 12 publications

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“…It is why the dimension reduction method is absolutely necessary. In an effort to divide the data into smaller, the PCA method is widely used in palmprint recognition [15]. However, the technique has drawbacks difficult to acknowledge the feature information in a single image that have a variation orientation.…”

Section: Proposed Methods 21 Image Enhancementmentioning

confidence: 99%

Combination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition

Kusban¹,

Susanto²,

Wahyunggoro³

2016

IJECE

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Palmprint identification is part of biometric recognition, which attracted many researchers, especially when fusion with face identification that will be applied in the airport to hasten knowing individual identity. To accelerate the process of verification feature palms, dimension reduction method is the dominant technique to extract the feature information of palms. The mechanism will boost if the ROI images are processed prior to get normalize image enhancement. In this paper with three sample input database, a kernel PCA method used as a dimension reduction compared with three others and a skeleton filter used as a image enhancement method compared with six others. The final results show that the proposed method successfully achieve the target in terms of the processing time of 0.7415 second, the EER performance rate of 0.19 % and the success of verification process about 99,82 %.

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Section: Proposed Methods 21 Image Enhancementmentioning

confidence: 99%

Combination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition

Kusban¹,

Susanto²,

Wahyunggoro³

2016

IJECE

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show abstract

“…To guarantee the accurate signal of a single modality system, the fusion of two or more signals is significant to improve the performance of the system [39,[41][42][43][44][45][46][47][48]. To achieve robust and discriminative performance for gender recognition, a fusion of EMG and HRV [40,41,49] is proposed before feeding into the interface system in order to control the level of MR valve stiffness of the stepper before the rehabilitation exercise can be emulated [40,41].…”

Section: Workmentioning

confidence: 99%

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

et al. 2017

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Gender recognition is trivial for a physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) (Hsu et al.) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SVM). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient and effective for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Barani's work (90.34%), Nazarloo's work (92.50%), and other classifiers.

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“…In addition to these advantages if two persons have similar faces, certainly their ears will be different. Furthermore if images of face profile exist, Ear can be extracted from them which makes cost low and time can be saved [2].…”

Section: Introductionmentioning

confidence: 99%

An Ear Recognition Method Based on Rotation Invariant Transformed DCT

Hourali

Gharravi

2017

IJECE

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Human recognition systems have gained great importance recently in a wide range of applications like access, control, criminal investigation and border security. Ear is an emerging biometric which has rich and stable structure and can potentially be implemented reliably and cost efficiently. Thus human ear recognition has been researched widely and made greatly progress. High recognition rates which are reported in most existing methods can be reached only under closely controlled conditions. Actually a slight amount of rotation and translation which is inescapable would be injurious for system performance. In this paper, a method that uses a transformed type of DCT is implemented to extract meaningful features from ear images. This algorithm is quite robust to ear rotation, translation and illumination. The proposed method is experimented on two popular databases, i.e. USTB II and IIT Delhi II, which achieves significant improvement in the performance in comparison to other methods with good efficiency based on LBP, DSIFT and Gabor. Also because of considering only important coefficients, this method is faster compared to other methods.

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Human Identification Based on ECG and Palmprint

Cited by 27 publications

References 12 publications

Combination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition

Combination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

An Ear Recognition Method Based on Rotation Invariant Transformed DCT

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