On Recognizing Occluded Faces in the Wild

Erakin, Mustafa Ekrem; Demir, Uğur; Ekenel, Hazım Kemal

doi:10.1109/biosig52210.2021.9548293

Cited by 18 publications

(5 citation statements)

References 14 publications

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“…• ROF [29]: this database consists of occluded faces with both upper face occlusion, due to sunglasses, and lower face occlusion, due to masks.…”

Section: Experimental Framework a Experimental Protocolmentioning

confidence: 99%

“…Surveillance Extreme Demographic Bias Pose Age Occlusions Average LFW [23] QUIS-CAMPI [24] TinyFaces [25] BUPT [26] CFP-FP [27] AgeDB [28] ROF [29] ArcFace [32] 96 [23] QUIS-CAMPI [24] TinyFaces [25] BUPT [26] CFP-FP [27] AgeDB [28] ROF [29] ArcFace [32] 6.70 2.20 We hypothesize that this reduction in performance might be produced as in the ChatGPT 4x3 case, the model needs first to detect the faces in the whole image, and then perform facial verification, potentially compromising overall task execution. Nevertheless, considering this matrix approximation could serve as an quick solution when the ChatGPT API imposes limitations on daily requests or when the budget to perform comparisons is low.…”

Section: Controlledmentioning

confidence: 99%

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How Good Is ChatGPT at Face Biometrics? A First Look Into Recognition, Soft Biometrics, and Explainability

Deandres-Tame,

Tolosana,

Vera-Rodriguez

et al. 2024

IEEE Access

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Large Language Models (LLMs) such as GPT developed by OpenAI, have already shown astonishing results, introducing quick changes in our society. This has been intensified by the release of ChatGPT which allows anyone to interact in a simple conversational way with LLMs, without any experience in the field needed. As a result, ChatGPT has been rapidly applied to many different tasks such as code-and song-writer, education, virtual assistants, etc., showing impressive results for tasks for which it was not trained (zero-shot learning). The present study aims to explore the ability of ChatGPT, based on the recent GPT-4 multimodal LLM, for the task of face biometrics. In particular, we analyze the ability of ChatGPT to perform tasks such as face verification, soft-biometrics estimation, and explainability of the results. ChatGPT could be very valuable to further increase the explainability and transparency of the automatic decisions in human scenarios. Experiments are carried out in order to evaluate the performance and robustness of ChatGPT, using popular public benchmarks and comparing the results with state-of-the-art methods in the field. The results achieved in this study show the potential of LLMs such as ChatGPT for face biometrics, especially to enhance explainability. For reproducibility reasons, we release all the code in GitHub.

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“…• ROF [29]: this database consists of occluded faces with both upper face occlusion, due to sunglasses, and lower face occlusion, due to masks.…”

Section: Experimental Framework a Experimental Protocolmentioning

confidence: 99%

Section: Controlledmentioning

confidence: 99%

How Good Is ChatGPT at Face Biometrics? A First Look Into Recognition, Soft Biometrics, and Explainability

Deandres-Tame,

Tolosana,

Vera-Rodriguez

et al. 2024

IEEE Access

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“…Boutros et al [17] proposed an approach that will transfer the template of a masked-face into an unmasked-like template through an on-the-top network trained with a specially proposed Selfrestrained Triplet Loss. Follow-up solutions trained FR models in a way that would promote producing similar face templates for masked and unmasked faces [18][19][20][21]. This interest in enhancing FR performance on masked faces led to two competitions that attracted a diverse set of academic and industrial participants [22,23].…”

Section: Related Workmentioning

confidence: 99%

Masked face recognition: Human versus machine

et al. 2022

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The recent COVID‐19 pandemic has increased the focus on hygienic and contactless identity verification methods. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition (FR) in a collaborative environment is a currently sensitive yet understudied issue. Recent reports have tackled this by evaluating the masked probe effect on the performance of automatic FR solutions. However, such solutions can fail in certain processes, leading to the verification task being performed by a human expert. This work provides a joint evaluation and in‐depth analyses of the face verification performance of human experts in comparison to state‐of‐the‐art automatic FR solutions. This involves an extensive evaluation by human experts and 4 automatic recognition solutions. The study concludes with a set of take‐home messages on different aspects of the correlation between the verification behaviour of humans and machines.

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“…The rank orders the solutions from the best to the worst within that protocol. [14] dataset that consists of sunglasses, masks and neutral face images. As an addition to the lower face occlusion dataset, the ROF dataset was combined with the MFR2 [1] dataset.…”

Section: Simit Lab Teammentioning

confidence: 99%

“…There are a few works on OFR post-pandemic. Erakiotan et al [14] proposed a new dataset composed of real faces occluded by sunglasses or face masks. Qiu et al [33] proposed an improved framework over the previous state-of-the-art method by Song et al [35].…”

Section: Introductionmentioning

confidence: 99%

OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusions

Neto¹,

Pinto²,

Damer³

et al. 2022

Preprint

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This work summarizes the IJCB Occluded Face Recognition Competition 2022 (IJCB-OCFR-2022) embraced by the 2022 International Joint Conference on Biometrics (IJCB 2022). OCFR-2022 attracted a total of 3 participating teams, from academia. Eventually, six valid submissions were submitted and then evaluated by the organizers. The competition was held to address the challenge of face recognition in the presence of severe face occlusions. The participants were free to use any training data and the testing data was built by the organisers by synthetically occluding parts of the face images using a well-known dataset. The submitted solutions presented innovations and performed very competitively with the considered baseline. A major output of this competition is a challenging, realistic, and diverse, and publicly available occluded face recognition benchmark with well defined evaluation protocols.

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On Recognizing Occluded Faces in the Wild

Cited by 18 publications

References 14 publications

How Good Is ChatGPT at Face Biometrics? A First Look Into Recognition, Soft Biometrics, and Explainability

How Good Is ChatGPT at Face Biometrics? A First Look Into Recognition, Soft Biometrics, and Explainability

Masked face recognition: Human versus machine

OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusions

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