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This paper introduces a new approach for human recognition using heart sounds.
Electrocardiogram (ECG) waveforms, for instance, have unique identity properties for human recognition, and their signals are not periodic.
Fingerprint matching has emerged as an effective tool for human recognition due to the uniqueness, universality and invariability of fingerprints.
In this paper, the proposed model is tested for human recognition based on the ear biometric measure.
Multimodal biometric systems are developed to address few of the limitations of unibiometric systems and works on the principle of using more than one biometric trait for human recognition.
For this reason, a comparison of techniques is presented in this paper, where the results achieved for ear recognition and face recognition by the proposed method are compared against a hierarchical genetic algorithm in order to know which of these techniques provides better results when a modular granular neural network is optimized and applied to pattern recognition mainly for human recognition.
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Iris recognition for human identification is one of the most accurate biometrics, and its employment is expanding globally.
Average human recognition rate for natural and acted speech amounted to 81 and 72%, respectively.
Besides natural language processing [19-21] [19-21]ave previousLTMseen used for haven activity recognition [22], image annotation and segmentation [23], and image-based object recognition [24].
This paper proposes a hybrid approach based on mutual adaptation for human gesture recognition.
We propose robust multi-dimensional motion features for human activity recognition from first-person videos.
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