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<p>Biometrics is a set of highly automated methods used for recognition purposes including analyzing physical traits of people and statistically measuring them. In forensic applications, fingerprint and face images are mostly used for recognition. There is a need during a criminal investigation to find out a face image of a person from its fingerprint. The proposed method able to identify the face of a person using an associated fingerprint. In this paper, Bregman divergence regularization is used to learn and optimize transferring subspace. This method first gleans knowledge from samples that are meant for training and transfer it to the testing samples. This regularization helps to minimize the probability distribution differences between two different domains. It helps to find a common subspace that boosts the performance of independent and identically distributed (i.i.d.) condition of samples. Practically the samples violate this i.i.d. condition. However, it will help to identify the correct suspect.</p><p>Biometrics is a set of highly automated methods used for recognition purposes including analyzing physical traits of people and statistically measuring them. In forensic applications, fingerprint and face images are mostly used for recognition. There is a need during a criminal investigation to find out a face image of a person from its fingerprint. The proposed method able to identify the face of a person using an associated fingerprint. In this paper, Bregman divergence regularization is used to learn and optimize transferring subspace. This method first gleans knowledge from samples that are meant for training and transfer it to the testing samples. This regularization helps to minimize the probability distribution differences between two different domains. It helps to find a common subspace that boosts the performance of independent and identically distributed (i.i.d.) condition of samples. Practically the samples violate this i.i.d. condition. However, it will help to identify the correct suspect.</p>

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Transfer learning for face recognition using fingerprint biometrics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月13日

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  • Rupali Kute,
  • Vibha Vyas,
  • Alwin Anuse

摘要

Biometrics is a set of highly automated methods used for recognition purposes including analyzing physical traits of people and statistically measuring them. In forensic applications, fingerprint and face images are mostly used for recognition. There is a need during a criminal investigation to find out a face image of a person from its fingerprint. The proposed method able to identify the face of a person using an associated fingerprint. In this paper, Bregman divergence regularization is used to learn and optimize transferring subspace. This method first gleans knowledge from samples that are meant for training and transfer it to the testing samples. This regularization helps to minimize the probability distribution differences between two different domains. It helps to find a common subspace that boosts the performance of independent and identically distributed (i.i.d.) condition of samples. Practically the samples violate this i.i.d. condition. However, it will help to identify the correct suspect.

Biometrics is a set of highly automated methods used for recognition purposes including analyzing physical traits of people and statistically measuring them. In forensic applications, fingerprint and face images are mostly used for recognition. There is a need during a criminal investigation to find out a face image of a person from its fingerprint. The proposed method able to identify the face of a person using an associated fingerprint. In this paper, Bregman divergence regularization is used to learn and optimize transferring subspace. This method first gleans knowledge from samples that are meant for training and transfer it to the testing samples. This regularization helps to minimize the probability distribution differences between two different domains. It helps to find a common subspace that boosts the performance of independent and identically distributed (i.i.d.) condition of samples. Practically the samples violate this i.i.d. condition. However, it will help to identify the correct suspect.