Toward Open-Set Face Recognition

Much research has been conducted on both face identification and face verification, with greater focus on the latter. Research on face identification has mostly focused on using closed-set protocols, which assume that all probe images used in evaluation contain identities of subjects that are enrolled in the gallery. Real systems, however, where only a fraction of probe sample identities are enrolled in the gallery, cannot make this closed-set assumption. Instead, they must assume an open set of probe samples and be able to reject/ignore those that correspond to unknown identities. In this paper, we address the widespread misconception that thresholding verification-like scores is a good way to solve the open-set face identification problem, by formulating an open-set face identification protocol and evaluating different strategies for assessing similarity. Our open-set identification protocol is based on the canonical labeled faces in the wild (LFW) dataset. Additionally to the known identities, we introduce the concepts of known unknowns (known, but uninteresting persons) and unknown unknowns (people never seen before) to the biometric community. We compare three algorithms for assessing similarity in a deep feature space under an open-set protocol: thresholded verification-like scores, linear discriminant analysis (LDA) scores, and an extreme value machine (EVM) probabilities. Our findings suggest that thresholding EVM probabilities, which are open-set by design, outperforms thresholding verification-like scores.Comment: Accepted for Publication in CVPR 2017 Biometrics Worksho

Expressiveness of Neural Networks Having Width Equal or Below the Input Dimension

The understanding about the minimum width of deep neural networks needed to ensure universal approximation for different activation functions has progressively been extended (Park et al., 2020). In particular, with respect to approximation on general compact sets in the input space, a network width less than or equal to the input dimension excludes universal approximation. In this work, we focus on network functions of width less than or equal to the latter critical bound. We prove that in this regime, the exact fit of partially constant functions on disjoint compact sets is still possible for ReLU network functions under some conditions on the mutual location of these components. We show that with cosine as activation function, a three layer network of width one is sufficient to approximate any function on arbitrary finite sets. Conversely, we prove a maximum principle from which we conclude that for all continuous and monotonic activation functions, universal approximation of arbitrary continuous functions is impossible on sets that coincide with the boundary of an open set plus an inner point

Can Creative Art Activities Contribute to Social Emotional Communication in Online Groups during the COVID 19 Pandemic?

In this paper, the authors review a single session in which a small group of participants from different countries within the Asian Pacific region used creative arts improvisation to develop collaborative expression of their subjective experiences during the COVID health crisis. During this review, the authors consider if meaningful communication could develop among the participants and how such exchanges might be expanded to contribute to communities and within an international context. The group was conducted online, and the members were from Guam, China and another woman from India currently studying in New Zealand. The improvisational expressions consisted of dance, vocal music, art, poetry, and fairy tale making followed by discussion. The general themes from this collection of images that emerged from the improvisations ranged from disconnection to positive connection towards each other and a renewal of hope. These developments occurred online and among people from different countries. Some of these participants did not know each other prior to the meeting and others did not share a primary language. The authors use this review to suggest some potential guidelines that might apply to other projects that address community responses to the current pandemic and possible cross-cultural connections during times of crisis

Autoencoder Attractors for Uncertainty Estimation

The reliability assessment of a machine learning model's prediction is an important quantity for the deployment in safety critical applications. Not only can it be used to detect novel sceneries, either as out-of-distribution or anomaly sample, but it also helps to determine deficiencies in the training data distribution. A lot of promising research directions have either proposed traditional methods like Gaussian processes or extended deep learning based approaches, for example, by interpreting them from a Bayesian point of view. In this work we propose a novel approach for uncertainty estimation based on autoencoder models: The recursive application of a previously trained autoencoder model can be interpreted as a dynamical system storing training examples as attractors. While input images close to known samples will converge to the same or similar attractor, input samples containing unknown features are unstable and converge to different training samples by potentially removing or changing characteristic features. The use of dropout during training and inference leads to a family of similar dynamical systems, each one being robust on samples close to the training distribution but unstable on new features. Either the model reliably removes these features or the resulting instability can be exploited to detect problematic input samples. We evaluate our approach on several dataset combinations as well as on an industrial application for occupant classification in the vehicle interior for which we additionally release a new synthetic dataset.Comment: This paper is accepted at IEEE International Conference on Pattern Recognition (ICPR), 202