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Registration-free Face-SSD: Single shot analysis of smiles, facial attributes, and affect in the wild

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)17-29
Number of pages13
JournalComputer Vision and Image Understanding
Early online date7 Feb 2019
DateAccepted/In press - 31 Jan 2019
DateE-pub ahead of print - 7 Feb 2019
DatePublished (current) - 1 May 2019


In this paper, we present a novel single shot face-related task analysis method, called Face-SSD, for detecting faces and for performing various face-related (classification/regression) tasks including smile recognition, face attribute prediction and valence-arousal estimation in the wild. Face-SSD uses a Fully Convolutional Neural Network (FCNN) to detect multiple faces of different sizes and recognise/regress one or more face-related classes. Face-SSD has two parallel branches that share the same low-level filters, one branch dealing with face detection and the other one with face analysis tasks. The outputs of both branches are spatially aligned heatmaps that are produced in parallel–therefore Face-SSD does not require that face detection, facial region extraction, size normalisation, and facial region processing are performed in subsequent steps. Our contributions are threefold: 1) Face-SSD is the first network to perform face analysis without relying on pre-processing such as face detection and registration in advance–Face-SSD is a simple and a single FCNN architecture simultaneously performing face detection and face-related task analysis–those are conventionally treated as separate consecutive tasks; 2) Face-SSD is a generalised architecture that is applicable for various face analysis tasks without modifying the network structure–this is in contrast to designing task-specific architectures; and 3) Face-SSD achieves real-time performance (21 FPS) even when detecting multiple faces and recognising multiple classes in a given image (300 × 300). Experimental results show that Face-SSD achieves state-of-the-art performance in various face analysis tasks by reaching a recognition accuracy of 95.76% for smile detection, 90.29% for attribute prediction, and Root Mean Square (RMS) error of 0.44 and 0.39 for valence and arousal estimation.

    Research areas

  • face analysis, Smile recognition, Facial attribute prediction, Affect recognition, Valence and arousal estimation, Single shot multiBox detector



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    Licence: CC BY-NC-ND


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