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  "document": {
    "abbrevTitle": "SMPTE Mot. Imag. J",
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      "version": "smpte-idams-publication@v1"
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    "abstract": "Existing computer vision-based emotion recognition systems are trained to classify images of faces into a very limited number of emotions. In this work, we take a different approach and train a convolutional neural network (CNN) that is able to distinguish subtle differences in facial expressions appearing in both individual images and videos. For this effect, we train a feature embedding network that maps a facial image into a position in embedding space, such that it is positioned close to similar facial expressions compared with other expressions. We train the feature embedding network using triplet loss on the publicly available facial expression comparison (FEC) data set. The proposed facial expression model is lightweight (4.7M parameters) and obtains a triplet prediction accuracy of 84.5%—very close to the average human performance of 86.2%. A key contribution of this work is adapting the model that has been trained on images to work reliably on video content. To this end, we propose a novel automatic face quality assessment (FaceQA), which allows us to filter out faces that are unsuited for expression analysis. We validate the proposed approach on several applications, including searching video content for a specific expression from a single query face, generating facial expression statistics of various actors appearing in a video, as well as generating facial expression summaries.",
    "abstract$meta": {
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    "authors": [
      {
        "bio": "Dominic Rüfenacht leads the science team and is co-founder at Mobius Labs, Berlin, Germany, where he is conducting research on efficient machine learning architectures that aim to understanding image and video content. He received both BSc and MSc in communication systems with specialization in “Signals, Images and Interfaces” from the Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Switzerland. He holds a PhD in electrical engineering from the UNSW Sydney, Sydney, NSW, Australia. His PhD thesis entitled “Novel Motion Anchoring Strategies for Wavelet-Based Highly Scalable Video Compression” won a Springer thesis award and was published as a book. Apart from deep learning for various image and visual analysis tasks, his research interests lie in computational photography and next-generation video compression.",
        "name": "Dominic Rüfenacht",
        "orcid": "0000-0002-1450-8070"
      },
      {
        "bio": "Appu Shaji is the chief executive officer and chief scientist at Mobius Labs (http://www.mobiuslabs.com). Prior to founding Mobius Labs, he was the head of research and development at EyeEm, where he led a team of top computer vision researchers from Europe, solving various visual classification and ranking problems. He also founded sight.io, where he developed technology to rate images based on computational aesthetics. sight.io was acquired by EyeEm in March 2014. Shaji has also held postdoctoral positions at the Image and Visual Representation Group (IVRG) and Computer Vision Lab (CVLAB), EPFL. He received a PhD in computer science and engineering from the IIT Bombay, Mumbai, India, for which he received the best PhD thesis award from the Computer Science (CS) department in 2009. His papers have appeared in top computer vision publications including IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
        "name": "Appu Shaji"
      }
    ],
    "authors$meta": {
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      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9749797/09749801.xml) via idamsIngest.js",
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      "version": "smpte-idams-publication@v1"
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    "contentType": "orig-research",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9749797/09749801.xml) via idamsIngest.js",
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      "version": "smpte-idams-publication@v1"
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    "copyright": {
      "holder": "SMPTE",
      "holder$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9749797/09749801.xml) via idamsIngest.js",
        "source": "parsed",
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        "version": "smpte-idams-publication@v1"
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      "year": "2022",
      "year$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9749797/09749801.xml) via idamsIngest.js",
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        "version": "smpte-idams-publication@v1"
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      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9749797/09749801.xml) via idamsIngest.js",
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    "docId": "10.5594-JMI.2022.3151144",
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      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9749797/09749801.xml) via idamsIngest.js",
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    "docLabel": "SMPTE Motion Imaging Journal ( Volume: 131, Issue: 3, April 2022)",
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      "source": "parsed",
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      "version": "smpte-idams-publication@v1"
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    "docTitle": "Customized Facial Expression Analysis in Video",
    "docTitle$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9749797/09749801.xml) via idamsIngest.js",
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    "docType": "Journal Article",
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    "doi": "10.5594/JMI.2022.3151144",
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    "href": "https://doi.org/10.5594/JMI.2022.3151144",
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    "issn": {
      "electronic": "2160-2492",
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      "print": "1545-0279",
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        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9749797/09749801.xml) via idamsIngest.js",
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    "issn$meta": {
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    "journalAcronym": "MIJR",
    "journalAcronym$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9749797/09749801.xml) via idamsIngest.js",
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    "journalTitle": "SMPTE Motion Imaging Journal",
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    "keywords": [
      "Facial Expression Analysis",
      "Face Quality Assessment",
      "One-shot Learning",
      "Real-time Video Analysis"
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    "keywords$meta": {
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      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9749797/09749801.xml) via idamsIngest.js",
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    "number": "3",
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    "pages": "17–24",
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    "publicationDate": "2022-04-01",
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    "publisher": "SMPTE",
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