Explainable Face Recognition

Williford, Jonathan R.; May, Brandon B.; Byrne, Jeffrey

doi:10.1007/978-3-030-58621-8_15

Cited by 65 publications

(15 citation statements)

References 30 publications

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“…Otherwise, a simple adversarial attack [3] may result in irreversible damage. Similar limitation also echoes in other critical applications, like autonomous vehicle [4] and face recognition [5], etc.…”

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confidence: 52%

Explainable Diabetic Retinopathy Detection and Retinal Image Generation

Niu,

Gu,

Zhao

et al. 2021

Preprint

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Though deep learning has shown successful performance in classifying the label and severity stage of certain diseases, most of them give few explanations on how to make predictions. Inspired by Koch's Postulates, the foundation in evidence-based medicine (EBM) to identify the pathogen, we propose to exploit the interpretability of deep learning application in medical diagnosis. By determining and isolating the neuron activation patterns on which diabetic retinopathy (DR) detector relies to make decisions, we demonstrate the direct relation between the isolated neuron activation and lesions for a pathological explanation. To be specific, we first define novel pathological descriptors using activated neurons of the DR detector to encode both spatial and appearance information of lesions. Then, to visualize the symptom encoded in the descriptor, we propose Patho-GAN, a new network to synthesize medically plausible retinal images. By manipulating these descriptors, we could even arbitrarily control the position, quantity, and categories of generated lesions.We also show that our synthesized images carry the symptoms directly related to diabetic retinopathy diagnosis. Our generated images are both qualitatively and quantitatively superior to the ones by previous methods. Besides, compared to existing methods that take hours to generate an image, our second level speed endows the potential to be an effective solution for data augmentation.Code is available at https://github.com/zzdyyy/ Patho-GAN.

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confidence: 52%

Explainable Diabetic Retinopathy Detection and Retinal Image Generation

Niu,

Gu,

Zhao

et al. 2021

Preprint

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“…Only a few early attempts have been made towards explainable facial processing systems. For example, an "explainable face recognition" (XFR) protocol is presented in 53 to generate an attention map highlighting the facial regions that are responsible for a matching. The work in 54 uses similar representations to explain how a deep neural network distinguishes facial expressions of pain from facial expressions of happiness and disgust.…”

Section: The Need For More Explainable Facial Processing Systemsmentioning

confidence: 99%

The Landscape of Facial Processing Applications in the Context of the European AI Act and the Development of Trustworthy Systems

Hupont

Tolan

Güneş

2022

Preprint

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This work focuses on facial processing, which refers to artificial intelligence (AI) systems that take facial images or videos as input data and perform some AI-driven processing to obtain higher-level information (e.g. a person’s identity, emotions, demographic attributes) or newly generated imagery (e.g. with modified facial attributes). Facial processing tasks, such as face detection, face identification, facial expression recognition or facial attribute manipulation, are generally studied as separate research fields and without considering a particular scenario, context of use or intended purpose. This paper studies the field of facial processing in a holistic manner. It establishes the landscape of key computational tasks, applications and industrial players in the field in order to identify the 60 most relevant applications adopted for real-world usages. These applications are analysed in the context of the new European AI Act and the 7 requirements for Trustworthy AI defined by the European High Level Expert Group on AI. More particularly, we assess the risk level conveyed by each application according to the AI Act and reflect on current research, technical and societal challenges towards trustworthy facial processing systems.

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“…Each diagram shows a different XAI use case (clockwise, starting from the lower left): XAI as preprocessing (eg, explaining the data), XAI as part of the system (eg, inherently interpretable models), XAI as post-hoc explanation (eg, visual saliency maps), and XAI as a combination of all the previous methods been an increasing push to create explanations for other image understanding tasks, including object detection 17 and image similarity. [18][19][20][21][22] A notional architecture diagram for an analytics Domain Implementation is shown in Figure 6, here supporting saliency maps as an example. On the client side (right), the framework provides hooks for configuration, supplying lists of images, receiving lists of saliency maps, etc.…”

Section: Analytics Domain Implementationmentioning

confidence: 99%

XAITK: The explainable AI toolkit

Tunison

Vasu

et al. 2021

Applied AI Letters

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Recent advances in artificial intelligence (AI), driven mainly by deep neural networks, have yielded remarkable progress in fields, such as computer vision, natural language processing, and reinforcement learning. Despite these successes, the inability to predict how AI systems will behave "in the wild" impacts almost all stages of planning and deployment, including research and development, verification and validation, and user trust and acceptance. The field of explainable artificial intelligence (XAI) seeks to develop techniques enabling AI algorithms to generate explanations of their results; generally these are human-interpretable representations or visualizations that are meant to "explain" how the system produced its outputs. We introduce the Explainable AI Toolkit (XAITK), a DARPA-sponsored effort that builds on results from the 4-year DARPA XAI program. The XAITK has two goals: (a) to consolidate research results from DARPA XAI into a single publicly accessible repository; and (b) to identify operationally relevant capabilities developed on DARPA XAI and assist in their transition to interested partners. We first describe the XAITK website and associated capabilities. These place the research results from DARPA XAI in the wider context of general research in the field of XAI, and include performer contributions of code, data, publications, and reports. We then describe the XAITK analytics and autonomy software frameworks. These are Python-based frameworks focused on particular XAI domains, and designed to provide a single integration endpoint for multiple algorithm implementations from across DARPA XAI. Each framework generalizes APIs for system-level data and control while providing a plugin interface for existing and future algorithm implementations. The XAITK project can be followed at: https://xaitk.org.

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Explainable Face Recognition

Cited by 65 publications

References 30 publications

Explainable Diabetic Retinopathy Detection and Retinal Image Generation

Explainable Diabetic Retinopathy Detection and Retinal Image Generation

The Landscape of Facial Processing Applications in the Context of the European AI Act and the Development of Trustworthy Systems

XAITK: The explainable AI toolkit

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