Single-shot face and landmarks detector
Facial landmark detection is an important sub-task in solving a number of biometric facial recognition tasks. In face recognition systems, the construction of a biometric template occurs according to a previously aligned (normalized) face image and the normalization stage includes the task of findin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Samara National Research University
2020-08-01
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| Series: | Компьютерная оптика |
| Subjects: | |
| Online Access: | http://computeroptics.smr.ru/KO/PDF/KO44-4/440413.pdf |
| _version_ | 1818522640572219392 |
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| author | Y.V. Vizilter V.S. Gorbatsevich A.S. Moiseenko |
| author_facet | Y.V. Vizilter V.S. Gorbatsevich A.S. Moiseenko |
| author_sort | Y.V. Vizilter |
| collection | DOAJ |
| description | Facial landmark detection is an important sub-task in solving a number of biometric facial recognition tasks. In face recognition systems, the construction of a biometric template occurs according to a previously aligned (normalized) face image and the normalization stage includes the task of finding facial keypoints. A balance between quality and speed of the facial keypoints detector is important in such a problem. This article proposes a CNN-based one-stage detector of faces and keypoints operating in real time and achieving high quality on a number of well-known test datasets (such as AFLW2000, COFW, Menpo2D). The proposed face and facial landmarks detector is based on the idea of a one-stage SSD object detector, which has established itself as an algorithm that provides high speed and high quality in object detection task. As a basic CNN architecture, we used the ShuffleNet V2 network. An important feature of the proposed algorithm is that the face and facial keypoint detection is done in one CNN forward pass, which can significantly save time at the implementation stage. Also, such multitasking allows one to reduce the percentage of errors in the facial keypoints detection task, which positively affects the final face recognition algorithm quality. |
| first_indexed | 2024-12-11T05:35:58Z |
| format | Article |
| id | doaj.art-2ce02e445da14cafb573892e035de85d |
| institution | Directory Open Access Journal |
| issn | 0134-2452 2412-6179 |
| language | English |
| last_indexed | 2024-12-11T05:35:58Z |
| publishDate | 2020-08-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj.art-2ce02e445da14cafb573892e035de85d2022-12-22T01:19:17ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792020-08-0144458959510.18287/2412-6179-CO-674Single-shot face and landmarks detectorY.V. Vizilter0V.S. Gorbatsevich1A.S. Moiseenko2State Research Institute of Aviation Systems (GosNIIAS), Moscow, RussiaState Research Institute of Aviation Systems (GosNIIAS), Moscow, RussiaState Research Institute of Aviation Systems (GosNIIAS), Moscow, Russia; Moscow Institute of Physics and Technology (MIPT), Moscow, RussiaFacial landmark detection is an important sub-task in solving a number of biometric facial recognition tasks. In face recognition systems, the construction of a biometric template occurs according to a previously aligned (normalized) face image and the normalization stage includes the task of finding facial keypoints. A balance between quality and speed of the facial keypoints detector is important in such a problem. This article proposes a CNN-based one-stage detector of faces and keypoints operating in real time and achieving high quality on a number of well-known test datasets (such as AFLW2000, COFW, Menpo2D). The proposed face and facial landmarks detector is based on the idea of a one-stage SSD object detector, which has established itself as an algorithm that provides high speed and high quality in object detection task. As a basic CNN architecture, we used the ShuffleNet V2 network. An important feature of the proposed algorithm is that the face and facial keypoint detection is done in one CNN forward pass, which can significantly save time at the implementation stage. Also, such multitasking allows one to reduce the percentage of errors in the facial keypoints detection task, which positively affects the final face recognition algorithm quality.http://computeroptics.smr.ru/KO/PDF/KO44-4/440413.pdfbiometryface detectioncnnlandmarks detectionssd |
| spellingShingle | Y.V. Vizilter V.S. Gorbatsevich A.S. Moiseenko Single-shot face and landmarks detector Компьютерная оптика biometry face detection cnn landmarks detection ssd |
| title | Single-shot face and landmarks detector |
| title_full | Single-shot face and landmarks detector |
| title_fullStr | Single-shot face and landmarks detector |
| title_full_unstemmed | Single-shot face and landmarks detector |
| title_short | Single-shot face and landmarks detector |
| title_sort | single shot face and landmarks detector |
| topic | biometry face detection cnn landmarks detection ssd |
| url | http://computeroptics.smr.ru/KO/PDF/KO44-4/440413.pdf |
| work_keys_str_mv | AT yvvizilter singleshotfaceandlandmarksdetector AT vsgorbatsevich singleshotfaceandlandmarksdetector AT asmoiseenko singleshotfaceandlandmarksdetector |