Secure Chaff-less Fuzzy Vault for Face Identification Systems

Dong, Xingbo; Kim, Soohyong; Jin, Zhe; Hwang, Jung Yeon; Cho, Sangrae; Teoh, Andrew Beng Jin

doi:10.1145/3442198

Cited by 16 publications

(15 citation statements)

References 35 publications

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“…The hashing methods considered thus far have used either cryptographic hash functions or non-cryptographic hashing techniques. A face BTP method that combines these two hashing approaches was presented in [29]. More specifically, [29] proposes a protected face identification system consisting of two sub-systems.…”

Section: A Non-nn Face Btp Methodsmentioning

confidence: 99%

“…A face BTP method that combines these two hashing approaches was presented in [29]. More specifically, [29] proposes a protected face identification system consisting of two sub-systems. The first sub-system compares the probe face template to each of the N reference face templates in the database, where all templates are protected by the noncryptographic Index-of-Max (IoM) hashing method proposed in [36] (which is a special instance of LSH).…”

Section: A Non-nn Face Btp Methodsmentioning

confidence: 99%

“…The most popular FaceNet model repository appears to be that of David Sandberg (https://bit.ly/3BFc2Fm), but this is not the only one (e.g., https://bit.ly/2WJTogQ presents another version). So, when authors do not point to a particular FaceNet implementation [8], [9], [25], [29] in Table V), the reproducibility of their work is reduced. In general, however, Table V shows that only about a third of the studied face BTP works did not provide a clear indication of where their adopted DNN models/architectures came from, whereas the remaining two thirds provided either a direct link or sufficient information to locate the adopted DNN (e.g., a citation of the DNN's paper, where a public repository link is provided).…”

Section: Methods Type Referencementioning

confidence: 99%

“…[12] Rank-1 IR (%) DET (FNIR vs. FPIR) (%) FNIR @ FPIR = {0, 0.1, 1} (%) [13] ROC (FRR vs. FAR, plus EER) (%) [14] TAR @ FAR = {1, 0.1, 0.01, 0.001} (%) ROC (TAR vs. FAR) [15] [16] {TP, FP, TN, FN} @ FPR = 0.001 [17] FNMR and FMR (%) ≈ [18] EER DET (Miss vs. False Alarm prob.) (%) ≈ [19] Accuracy (%) [20] EER (%) [21] Recognition rate (%) [22] Accuracy (%) DET (FRR vs. FAR, plus EER) (%) [23] AUC (%), EER (%), TPR (%), TAR @ FAR = 0.1 (%), Rank-1 DIR @ FAR = 1 (%) [24] EER (%), GAR @ FAR (%) [25] FAR and FRR (%) [26] EER (%) [27] EER ROC (1 -FNMR vs. FMR) [28] TAR @ FAR (%) [29] IR (%) @ Rank = {1, 10, 50}, ROC (VR vs. FAR) (%) ≈ Accuracy (%), Verification Recognition @ FAR = 0.1 (%), Rank-1 DIR @ FAR = 1 (%), Own metrics: TIR, MIR, FIR (%) [30] Accuracy, FAR, FRR, EER (%) [31] 1 -FNMR (TMR) @ FMR = 10 −3 (or 0.1%) ROC (1 -FNMR vs. FMR) NN-learned [37], [38] EER (%), GAR @ FAR = {0, 1} (%) [39] EER (%), GAR @ FAR = 1 (%) ROC (GAR vs. FAR) [40] EER (%), GAR @ FAR = {0, 0.01, 0.1} (%) ROC (GAR vs. FAR) [41] EER (%), GAR @ FAR = 0 (%) [42] EER (%), GAR @ FAR (%) [43] FAR and FRR (%) [44] EER [45] EER (%), GAR @ FAR = 0.01 (%) ROC (GAR vs. FAR) (%) [46] Accuracy (%), GAR @ FAR = 0 (%) ROC (TAR vs. FAR) [47] EER (%), GAR @ FAR = 0.1 (%), Maximum Average Precision [48] GAR @ FAR = 0.1 (%) [49] EER (%), FNMR @ FMR = 0.1 (%) DET (FNMR vs. FMR) [50] EER (%) [51] EER (%) ROC (TPR vs. FPR) of the "other" systems (i.e., those using unprotected templates or templates protected by other BTP methods) were deliber-ately/directly compared (e.g., in a table, on the same plot, or conceptually). On the other hand, an implicit/pa...…”

Section: Methods Type Referencementioning

confidence: 99%

“…[6]- [12] [13]- [17], [18], [20], [23], [19], [21], [22] [24], [28], [29] [25]- [27], [29] [30], [31] B. NN-learned Face BTP Methods…”

Section: Feature-levelmentioning

confidence: 99%

See 4 more Smart Citations

Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques

Hahn¹,

Marcel²

2021

Preprint

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As automated face recognition applications tend towards ubiquity, there is a growing need to secure the sensitive face data used within these systems. This paper presents a survey of biometric template protection (BTP) methods proposed for securing face "templates" (images or representative features) in neural-network-based face recognition systems. The BTP methods are categorised into two types: Non-NN and NN-learned. Non-NN methods use a neural network (NN) as a feature extractor, but the BTP part is based on a non-NN algorithm applied at either image-level or feature-level. In contrast, NN-learned methods specifically employ a NN to learn a protected template from the unprotected face image or features. We present examples of Non-NN and NN-learned face BTP methods from the literature, along with a discussion of the two categories' comparative strengths and weaknesses. We also investigate the techniques used to evaluate these BTP methods, in terms of the three most common BTP criteria: "recognition accuracy", "irreversibility", and "renewability/unlinkability". As expected, the recognition accuracy of protected face recognition systems is generally evaluated using the same (empirical) techniques employed for evaluating standard (unprotected) biometric systems. On the contrary, most irreversibility and renewability/unlinkability evaluations are found to be based on theoretical assumptions/estimates or verbal implications, with a lack of empirical validation in a practical face recognition context. We recommend, therefore, a greater focus on empirical evaluation strategies, to provide more concrete insights into the irreversibility and renewability/unlinkability of face BTP methods in practice. Additionally, an exploration of the reproducibility of the studied BTP works, in terms of the public availability of their implementation code and evaluation datasets/procedures, suggests that it would currently be difficult for the BTP community to faithfully replicate (and thus validate) most of the reported findings. So, we advocate for a push towards reproducibility, in the hope of furthering our understanding of the face BTP research field.

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Section: A Non-nn Face Btp Methodsmentioning

confidence: 99%