This paper introduces SEEKr, a novel approach for improving the efficiency of Knowledge Distillation (KD) in Face Recognition (FR) models. State-of-the-art FR systems achieve strong performance. However, they incur substantial computational costs, particularly during the transfer of knowledge from large, high-capacity teacher models (T) to compact student models (S) using massive training datasets containing millions of samples. SEEKr addresses this challenge by accelerating KD through the selection of a compact yet highly informative subset of the training data. The central insight of SEEKr is to prioritize hard samples, inputs that are challenging for the student, during the distillation process. Emphasizing these samples allows the student to more accurately approximate the teacher's decision function while learning from diverse and informative examples. In contrast, easy samples are shown to contribute marginally to effective knowledge transfer and can be safely excluded without notable performance degradation. By selectively distilling knowledge from hard samples, SEEKr significantly reduces the number of training samples required for KD, thus substantially lowering computational cost. Extensive experiments on multiple large-scale Face Recognition datasets, across diverse teacher architectures and sample selection strategies, demonstrate that SEEKr consistently achieves competitive or superior distillation performance while significantly reducing computational overhead. The proposed method is general, easily integrable into existing KD frameworks, and offers a scalable solution for efficient Face Recognition model training in resource-constrained environments. Code and pretrained models are available at https://github.com/jankolf/SEEKr.

Face Image Quality Assessment (FIQA) is crucial for reliable face recognition (FR) systems, yet existing Vision Transformer (ViT)-based approaches rely exclusively on final-layer representations, ignoring quality-relevant information captured at intermediate network depths. This paper presents the first comprehensive investigation of how intermediate representations within ViTs contribute to face quality assessment through early exit mechanisms and score fusion strategies. We systematically analyze all twelve transformer blocks of ViT-FIQA architectures, demonstrating that different depths capture distinct and complementary quality-relevant information, as evidenced by varying attention patterns and performance characteristics across network layers. Leveraging these insights, we propose a score fusion framework that combines quality predictions from multiple transformer blocks without architectural modifications or additional training. Our early exit analysis reveals optimal performance-efficiency tradeoffs, enabling significant computational savings while maintaining competitive performance. Through extensive evaluation across eight benchmark datasets using four FR models, we demonstrate that our fusion strategy improves upon single-exit approaches. Our proposed quality fusion approach employs depth-weighted averaging that assigns progressively higher importance to deeper transformer blocks, achieving the best quality assessment performance by effectively leveraging the hierarchical nature of feature learning in ViTs. Our work challenges the conventional wisdom that only deep features matter for face analysis, revealing that intermediate representations contain valuable information for quality assessment. The proposed framework offers practical benefits for realworld biometric systems by enabling adaptive computation based on resource constraints while maintaining competitive quality assessment capabilities. The implementation is publicly available at: https://github.com/gurayozgur/EX-FIQA.

Face Image Quality Assessment (FIQA) aims to assess the recognition utility of face samples and is essential for reliable face recognition (FR) systems. Existing approaches require computationally expensive procedures such as multiple forward passes, backpropagation, or additional training, and only recent work has focused on the use of Vision Transformers. Recent studies highlighted that these architectures inherently function as saliency learners with attention patterns naturally encoding spatial importance. This work proposes ATTN-FIQA, a novel training-free approach that investigates whether presoftmax attention scores from pre-trained Vision Transformerbased face recognition models can serve as quality indicators. We hypothesize that attention magnitudes intrinsically encode quality: high-quality images with discriminative facial features enable strong query-key alignments producing focused, highmagnitude attention patterns, while degraded images generate diffuse, low-magnitude patterns. ATTN-FIQA extracts presoftmax attention matrices from the final transformer block, aggregate multi-head attention information across all patches, and compute image-level quality scores through simple averaging, requiring only a single forward pass through pre-trained models without architectural modifications, backpropagation, or additional training. Through comprehensive evaluation across eight benchmark datasets and four FR models, this work demonstrates that attention-based quality scores effectively correlate with face image quality and provide spatial interpretability, revealing which facial regions contribute most to quality determination. The implementation is publicly available at: https://github.com/gurayozgur/ATTN-FIQA.

Face recognition models have made substantial progress due to advances in deep learning and the availability of large-scale datasets. However, reliance on massive annotated datasets introduces challenges related to training computational cost and data storage, as well as potential privacy concerns regarding managing large face datasets. This paper presents DiffProb, the first data pruning approach for the application of face recognition. DiffProb assesses the prediction probabilities of training samples within each identity and prunes the ones with identical or close prediction probability values, as they are likely reinforcing the same decision boundaries, and thus contribute minimally with new information. We further enhance this process with an auxiliary cleaning mechanism to eliminate mislabeled and label-flipped samples, boosting data quality with minimal loss. Extensive experiments on CASIA-WebFace with different pruning ratios and multiple benchmarks, including LFW, CFP-FP, and IJB-C, demonstrate that DiffProb can prune up to $50 \%$ of the dataset while maintaining or even, in some settings, improving the verification accuracies. Additionally, we demonstrate DiffProb’s robustness across different architectures and loss functions. Our method significantly reduces training cost and data volume, enabling efficient face recognition training and reducing the reliance on massive datasets and their demanding management. The code, pretrained models, and pruned datasets are publicly released: https://github.com/EduardaCaldeira/DiffProb.

Current periocular and face recognition approaches utilize computationally costly deep neural networks, achieving notable recognition accuracies. Deploying such solutions in applications with limited computational resources requires minimizing their computational demand while maintaining similar recognition accuracies. Model compression techniques like model quantization can be used to reduce the computational costs of deep models. This approach is widely studied and applied to different machine-learning tasks, however it is understudied and investigated for biometrics. We propose in this work to reduce the computational cost of face and periocular recognition models using fixed- and mixed-precision model quantization. Specifically, we first quantize the full-precision models to fixed 8 and 6 bits, reducing the required memory footprint by 5x while maintaining, to a very large degree, the recognition accuracies. However, our achieved results demonstrated that by quantizing the models to extremely low b bits, e.g., below 6 bits, the accuracies significantly dropped, which motivated our investigation on mixed-precision quantization. Hence, we propose to utilize an iterative mixed-precision quantization scheme. In each iteration, the least important parameters are selected based on their weight magnitude and quantized to low b-bit precision and the model is fine-tuned. This approach is repeated until all parameters are quantized to low b-bit precision, achieving extreme reduction in memory footprint, e.g., 16x times, without significant loss in the model accuracies. The effectiveness of mixed- and fixed-precision quantization for biometric recognition models is studied and proved for two modalities, face and periocular, using three different deep network architectures and using different b bit precision.

This paper presents the summary of the Sclera Segmentation and Joint Recognition Benchmarking Competition (SSRBC 2023) held in conjunction with IEEE International Joint Conference on Biometrics (IJCB 2023). Different from the previous editions of the competition, SSRBC 2023 not only explored the performance of the latest and most advanced sclera segmentation models, but also studied the impact of segmentation quality on recognition performance. Five groups took part in SSRBC 2023 and submitted a total of six segmentation models and one recognition technique for scoring. The submitted solutions included a wide variety of conceptually diverse deep-learning models and were rigorously tested on three publicly available datasets, i.e., MASD, SBVPI and MOBIUS. Most of the segmentation models achieved encouraging segmentation and recognition performance. Most importantly, we observed that better segmentation results always translate into better verification performance.

The paper provides a summary of the 2023 Unconstrained Ear Recognition Challenge (UERC), a benchmarking effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current ear recognition techniques on a challenging ear dataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods and deep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences in gender and ethnicity. The results of the challenge suggest that a wide variety of models (e.g., transformers, convolutional neural networks, ensemble models) is capable of achieving competitive recognition results, but also that all of the models still exhibit considerable performance differentials with respect to both gender and ethnicity. To promote further development of unbiased and effective ear recognition models, the starter kit of UERC 2023 together with the baseline model, and training and test data is made available from: http://ears.fri.uni-lj.si/

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

State-of-the-art face recognition (FR) systems are based on overparameterized deep neural networks (DNN) which commonly use face images with 2563colors. The use of DNN and the storage of face images as references for comparison are limited in resource-restricted domains, which are hemmed in storage and computational capacity. A possible solution is to store the image only as a feature, which renders the human evaluation of the image impossible and forces the use of a single DNN (vendor) across systems. In this paper, we present a novel study on the possibility and effect of image color quantization on FR performance and storage efficiency. We leverage our conclusions to propose harmonizing the color quantization with the low-bit quantization of FR models. This combination significantly reduces the bits required to represent both the image and the FR model. In an extensive experiment on diverse sets of DNN architectures and color quantization steps, we validate on multiple benchmarks that the proposed methodology can successfully reduce the number of bits required for image pixels and DNN data while maintaining nearly equal recognition rates. The code and pre-trained models are available at https://github.com/jankolf/ColorQuantization.

Liveness Detection (LivDet) is an international competition series open to academia and industry with the objective to assess and report state-of-the-art in Presentation Attack Detection (PAD). LivDet-2023 Noncontact Fingerprint is the first edition of the noncontact fingerprint-based PAD competition for algorithms and systems. The competition serves as an important benchmark in noncontact-based fingerprint PAD, offering (a) independent assessment of the state-of-the-art in noncontact-based fingerprint PAD for algorithms and systems, and (b) common evaluation protocol, which includes finger photos of a variety of Presentation Attack Instruments (PAIs) and live fingers to the biometric research community (c) provides standard algorithm and system evaluation protocols, along with the comparative analysis of state-of-the-art algorithms from academia and industry with both old and new android smartphones. The winning algorithm achieved an APCER of 11.35% averaged over all PAIs and a BPCER of 0.62%. The winning system achieved an APCER of 13.0.4%, averaged over all PAIs tested over all the smartphones, and a BPCER of 1.68% over all smartphones tested. Four-finger systems that make individual finger-based PAD decisions were also tested. The dataset used for competition will be available1, to all researchers as per data share protocol.1https://noncontactfingerprint2023.1ivdet.org/index.php

Deep learning-based systems for periocular recognition make use of the high recognition performance of neural networks, which, however, is accompanied by high computational costs and memory footprints. This can lead to deployability problems, especially in mobile devices and embedded systems. Few previous works strived towards building lighter models, however, while still depending on floating-point numbers associated with higher computational cost and memory footprint. In this paper, we propose to adapt model quantization for periocular recognition. This, within the proposed scheme, leads to reducing the memory footprint of periocular recognition network by up to five folds while maintaining high recognition performance. We present a comprehensive analysis over three backbones and diverse experimental protocols to stress the consistency of our conclusions, along with a comparison with a wide set of baselines that prove the optimal trade-off between performance and model size achieved by our proposed solution. The code and pre-trained models have been made available at https://github.com/jankolf/ijcb-periocular-quantization.

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the topperforming academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.

Soft-biometrics play an important role in face biometrics and related fields since these might lead to biased performances, threaten the user's privacy, or are valuable for commercial aspects. Current face databases are specifically constructed for the development of face recognition applications. Consequently, these databases contain a large number of face images but lack in the number of attribute annotations and the overall annotation correctness. In this work, we propose a novel annotation-transfer pipeline that allows to accurately transfer attribute annotations from multiple source datasets to a target dataset. The transfer is based on a massive attribute classifier that can accurately state its prediction confidence. Using these prediction confidences, a high correctness of the transferred annotations is ensured. Applying this pipeline to the VGGFace2 database, we propose the MAAD-Face annotation database. It consists of 3.3M faces of over 9k individuals and provides 123.9M attribute annotations of 47 different binary attributes. Consequently, it provides 15 and 137 times more attribute annotations than CelebA and LFW. Our investigation on the annotation quality by three human evaluators demonstrated the superiority of the MAAD-Face annotations over existing databases. Additionally, we make use of the large number of high-quality annotations from MAAD-Face to study the viability of soft-biometrics for recognition, providing insights into which attributes support genuine and imposter decisions. The MAAD-Face annotations dataset is publicly available.

Face image quality is an important factor to enable high-performance face recognition systems. Face quality assessment aims at estimating the suitability of a face image for the purpose of recognition. Previous work proposed supervised solutions that require artificially or human labelled quality values. However, both labelling mechanisms are error prone as they do not rely on a clear definition of quality and may not know the best characteristics for the utilized face recognition system. Avoiding the use of inaccurate quality labels, we proposed a novel concept to measure face quality based on an arbitrary face recognition model. By determining the embedding variations generated from random subnetworks of a face model, the robustness of a sample representation and thus, its quality is estimated. The experiments are conducted in a cross-database evaluation setting on three publicly available databases. We compare our proposed solution on two face embeddings against six state-of-the-art approaches from academia and industry. The results show that our unsupervised solution outperforms all other approaches in the majority of the investigated scenarios. In contrast to previous works, the proposed solution shows a stable performance over all scenarios. Utilizing the deployed face recognition model for our face quality assessment methodology avoids the training phase completely and further outperforms all baseline approaches by a large margin. Our solution can be easily integrated into current face recognition systems, and can be modified to other tasks beyond face recognition.

Face quality assessment aims at estimating the utility of a face image for the purpose of recognition. It is a key factor to achieve high face recognition performances. Currently, the high performance of these face recognition systems come with the cost of a strong bias against demographic and non-demographic sub-groups. Recent work has shown that face quality assessment algorithms should adapt to the deployed face recognition system, in order to achieve highly accurate and robust quality estimations. However, this could lead to a bias transfer towards the face quality assessment leading to discriminatory effects e.g. during enrolment. In this work, we present an in-depth analysis of the correlation between bias in face recognition and face quality assessment. Experiments were conducted on two publicly available datasets captured under controlled and uncontrolled circumstances with two popular face embeddings. We evaluated four state-of-the-art solutions for face quality assessment towards biases to pose, ethnicity, and age. The experiments showed that the face quality assessment solutions assign significantly lower quality values towards subgroups affected by the recognition bias demonstrating that these approaches are biased as well. This raises ethical questions towards fairness and discrimination which future works have to address.