Expression invariant face recognition using semidecimated DWT, Patch-LDSMT, feature and score level fusion

Patil, Hemprasad Yashwant; Kothari, Ashwin; Bhurchandi, Kishor M.

doi:10.1007/s10489-015-0735-1

Cited by 13 publications

(9 citation statements)

References 48 publications

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“…This aims at suppressing the gender of the face image. Othman and Ross presented a different approach [31] where they proposed a face morphing methodology that iteratively morphs two images and therefore, suppresses gender information at different levels. However, this resulted in morphed images with significant artefacts.…”

Section: Related Workmentioning

confidence: 99%

“…To verify a subject's identity, a template of this subject probe is computed and compared against the template of the claimed identity [33]. Recent works showed that more information than just the person's identity can be deduced from these templates [10], [49].…”

Section: Introductionmentioning

confidence: 99%

“…This is further challenged by maintaining a high recognition performance at the same time. Previous works proposed privacy-enhancing solutions based on supervised [25], [28], [31] and unsupervised approaches [46], [47]. While unsupervised approaches show a more comprehensive but weaker privacy-enhancement, supervised approaches are limited to the suppression of pre-defined attributes and thus, are vulnerable to unconsidered function creep attacks.…”

Section: Introductionmentioning

confidence: 99%

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PE-MIU: A Training-Free Privacy-Enhancing Face Recognition Approach Based on Minimum Information Units

et al. 2020

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Research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric data. Utilizing biometric templates only, information about a persons gender, age, ethnicity, sexual orientation, and health state can be deduced. For many applications, these templates are expected to be used for recognition purposes only. Thus, extracting this information raises major privacy issues. Previous work proposed two kinds of learning-based solutions for this problem. The first ones provide strong privacy-enhancements, but limited to pre-defined attributes. The second ones achieve more comprehensive but weaker privacy-improvements. In this work, we propose a Privacy-Enhancing face recognition approach based on Minimum Information Units (PE-MIU). PE-MIU, as we demonstrate in this work, is a privacy-enhancement approach for face recognition templates that achieves strong privacy-improvements and is not limited to pre-defined attributes. We exploit the structural differences between face recognition and facial attribute estimation by creating templates in a mixed representation of minimal information units. These representations contain pattern of privacy-sensitive attributes in a highly randomized form. Therefore, the estimation of these attributes becomes hard for function creep attacks. During verification, these units of a probe template are assigned to the units of a reference template by solving an optimal best-matching problem. This allows our approach to maintain a high recognition ability. The experiments are conducted on three publicly available datasets and with five state-of-the-art approaches. Moreover, we conduct the experiments simulating an attacker that knows and adapts to the systems privacy mechanism. The experiments demonstrate that PE-MIU is able to suppress privacy-sensitive information to a significantly higher degree than previous work in all investigated scenarios. At the same time, our solution is able to achieve a verification performance close to that of the unmodified recognition system. Unlike previous works, our approach offers a strong and comprehensive privacy-enhancement without the need of training.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PE-MIU: A Training-Free Privacy-Enhancing Face Recognition Approach Based on Minimum Information Units

et al. 2020

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“…In Figure 8, the result obtained from the investigated work is compared with other techniques. The recognition rates of SVD, LLE-Eigen, FLD-PCA-ANN [13] and method in [8] are 92.96%, 93.93%, 84.9 and 96.6% respectively whereas for the proposed method the recognition rate is 97.3% which outperforms all the other methods discussed. Similarly for the CK database, neutral image is chosen as the reference image per person and rest of the images are used for testing purpose.…”

Section: Comparison With State-of-art Algorithmsmentioning

confidence: 80%

“…Many researchers have investigated methods to improve the face recognition by removing the facial expressions to obtain a neutral face i.e. making the face expression-invariant [6][7][8][9][10][11][12][13]. Hence to develop a robust face recognition algorithm which is insensitive to expression variations is one of the greatest challenges in this field.…”

Section: Figure 1 Typical Applications Of Face Recognition [4]mentioning

confidence: 99%