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Abstract

Texture features and stability have generated significant interest in biometric recognition. The inner knuckle print is distinctive and difficult to fake, making it extensively used in individual identification, criminal investigation, and various other domains. In recent years, the rapid progress of deep learning technology has created new prospects for internal knuckle recognition. This paper proposes a robust inner-knuckle-print recognition system (RIKP-RS) depending on two deep learning (DL) models. This paper focuses on the key components of the inner surface of the hand namely the little finger, ring finger, middle finger, index finger, and thumb finger that are used for human identification. Using the new segmentation method, rely on the Hands Landmark Module (MediaPipe Module) to detect components that have important biometric features. By Considering the inner knuckle print (IKP) as a texture, this study adopts two effective models: The DenseNet201 model and the InceptionV3 model to extract distinctive features from every modality. Uses all the key points of inner knuckle prints (IKP) of ten fingers for concatenated fusion recognition of all the features extracted by these models. Ultimately, these features are classified by different similarity metrics that are employed to compute the matching procedure for each model individually. A dataset of 11,076K hands with left and right palms was used to evaluate the proposed system. The system achieved the best performance on this dataset with a rank-1 score of 98.45% on the denseNet201 model, a rank-1 score of 99.81% on the inceptionV3 model for all left IKP, a rank-1 score of 96.68% on the denseNet201 model, and rank-1 score of 98.32% on the inceptionV3 model for all right IKP. These results cover the inceptionV3 model for all concatenated fusion recognition. In terms of performance, the RIKP-RS outperforms the most advanced inner knuckles pattern (IKP) recognition systems.

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