Matches in SemOpenAlex for { <https://semopenalex.org/work/W3019410392> ?p ?o ?g. }
- W3019410392 endingPage "1071" @default.
- W3019410392 startingPage "1057" @default.
- W3019410392 abstract "Facial expression recognition (FER) is crucial for social communication. However, current studies present limitations when addressing facial expression difference due to demographic variation, such as race, gender, and age, etc. In this article, we first propose a deeply-supervised attention network (DSAN) to recognize human emotions based on facial images automatically. Based on DSAN, a two-stage training scheme is designed, taking full advantage of the race/gender/age-related information. In our DSAN framework, multi-scale features are leveraged to capture more discriminative information from the deep layers to the shallow layers. Furthermore, we adopt the attention block to highlight the essential local facial characteristics; it performs well when it is incorporated into the deeply-supervised framework. Finally, we combine the complementary characteristics of multiple convolutional layers in deeply-supervised manner and ensemble the intermediate predicted scores. Our experimental results have shown that our proposed framework can (i) effectively integrate demographic information in improving the performance of a variety of FER tasks, (ii) learn informative feature representations with a visual explanation by capturing the regions of interests (ROI), (iii) achieve superior performance for both the posed and the spontaneous FER databases, each containing pictures of human facial expressions varied in gender, age or race." @default.
- W3019410392 created "2020-05-01" @default.
- W3019410392 creator A5015828577 @default.
- W3019410392 creator A5057139241 @default.
- W3019410392 creator A5072087245 @default.
- W3019410392 date "2022-04-01" @default.
- W3019410392 modified "2023-10-10" @default.
- W3019410392 title "Facial Expression Recognition With Deeply-Supervised Attention Network" @default.
- W3019410392 cites W114517082 @default.
- W3019410392 cites W1661563386 @default.
- W3019410392 cites W1903029394 @default.
- W3019410392 cites W1974210421 @default.
- W3019410392 cites W1981918162 @default.
- W3019410392 cites W2003238582 @default.
- W3019410392 cites W2004905543 @default.
- W3019410392 cites W2035372623 @default.
- W3019410392 cites W2051297709 @default.
- W3019410392 cites W2054493438 @default.
- W3019410392 cites W2067789110 @default.
- W3019410392 cites W2076807218 @default.
- W3019410392 cites W2100307458 @default.
- W3019410392 cites W2103943262 @default.
- W3019410392 cites W2117539524 @default.
- W3019410392 cites W2134860945 @default.
- W3019410392 cites W2147318340 @default.
- W3019410392 cites W2155893237 @default.
- W3019410392 cites W2194775991 @default.
- W3019410392 cites W2217426128 @default.
- W3019410392 cites W2244142460 @default.
- W3019410392 cites W2295107390 @default.
- W3019410392 cites W2421475762 @default.
- W3019410392 cites W2490049321 @default.
- W3019410392 cites W2550553598 @default.
- W3019410392 cites W2566855717 @default.
- W3019410392 cites W2617750261 @default.
- W3019410392 cites W2730601341 @default.
- W3019410392 cites W2737226596 @default.
- W3019410392 cites W2738672149 @default.
- W3019410392 cites W2767415038 @default.
- W3019410392 cites W2768634781 @default.
- W3019410392 cites W2798583514 @default.
- W3019410392 cites W2798734012 @default.
- W3019410392 cites W2798764454 @default.
- W3019410392 cites W2799151537 @default.
- W3019410392 cites W2800840848 @default.
- W3019410392 cites W2805810266 @default.
- W3019410392 cites W2884048435 @default.
- W3019410392 cites W2891191887 @default.
- W3019410392 cites W2894837309 @default.
- W3019410392 cites W2894871570 @default.
- W3019410392 cites W2904483377 @default.
- W3019410392 cites W2916197620 @default.
- W3019410392 cites W2963112684 @default.
- W3019410392 cites W2963252191 @default.
- W3019410392 cites W2963299740 @default.
- W3019410392 cites W2963495494 @default.
- W3019410392 cites W2963623198 @default.
- W3019410392 cites W2963685207 @default.
- W3019410392 cites W2963712289 @default.
- W3019410392 cites W2963813458 @default.
- W3019410392 cites W2964322530 @default.
- W3019410392 cites W2970303069 @default.
- W3019410392 cites W4252545733 @default.
- W3019410392 doi "https://doi.org/10.1109/taffc.2020.2988264" @default.
- W3019410392 hasPublicationYear "2022" @default.
- W3019410392 type Work @default.
- W3019410392 sameAs 3019410392 @default.
- W3019410392 citedByCount "33" @default.
- W3019410392 countsByYear W30194103922020 @default.
- W3019410392 countsByYear W30194103922021 @default.
- W3019410392 countsByYear W30194103922022 @default.
- W3019410392 countsByYear W30194103922023 @default.
- W3019410392 crossrefType "journal-article" @default.
- W3019410392 hasAuthorship W3019410392A5015828577 @default.
- W3019410392 hasAuthorship W3019410392A5057139241 @default.
- W3019410392 hasAuthorship W3019410392A5072087245 @default.
- W3019410392 hasConcept C119857082 @default.
- W3019410392 hasConcept C121332964 @default.
- W3019410392 hasConcept C134306372 @default.
- W3019410392 hasConcept C136197465 @default.
- W3019410392 hasConcept C138885662 @default.
- W3019410392 hasConcept C153180895 @default.
- W3019410392 hasConcept C154945302 @default.
- W3019410392 hasConcept C195704467 @default.
- W3019410392 hasConcept C199360897 @default.
- W3019410392 hasConcept C2524010 @default.
- W3019410392 hasConcept C2776401178 @default.
- W3019410392 hasConcept C2777210771 @default.
- W3019410392 hasConcept C2778334786 @default.
- W3019410392 hasConcept C28490314 @default.
- W3019410392 hasConcept C33923547 @default.
- W3019410392 hasConcept C41008148 @default.
- W3019410392 hasConcept C41895202 @default.
- W3019410392 hasConcept C44870925 @default.
- W3019410392 hasConcept C77618280 @default.
- W3019410392 hasConcept C81363708 @default.
- W3019410392 hasConcept C90559484 @default.
- W3019410392 hasConcept C97931131 @default.