Gender recognition of Guanyin in China based on VGGNet
Abstract Gender transformation of Guanyin (Avalokitesvara in India) in China is an intrinsically fascinating research topic. Besides the inner source from the scriptures, literatures and epigraphs, iconological analysis is usually as the external evidence of Guanyin’s gender recognition. However, th...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-06-01
|
| Series: | Heritage Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40494-022-00732-3 |
| _version_ | 1811227254584246272 |
|---|---|
| author | Yongwen Huang Dingding Chen Haiyan Wang Lulu Wang |
| author_facet | Yongwen Huang Dingding Chen Haiyan Wang Lulu Wang |
| author_sort | Yongwen Huang |
| collection | DOAJ |
| description | Abstract Gender transformation of Guanyin (Avalokitesvara in India) in China is an intrinsically fascinating research topic. Besides the inner source from the scriptures, literatures and epigraphs, iconological analysis is usually as the external evidence of Guanyin’s gender recognition. However, the ambiguous gender of the Guanyin image is often intentional and can be objectively assessed. Can computer vision be applied to the recognition objectively and quantitatively? In this paper, VGGNet (VGGNet is a very deep convolutional network for large-scale image recognition proposed by Visual Geometry Group of Oxford University) is applied to propose an automatic gender recognition system. To validate its efficiency, abundant experiments are implemented on the images of Dazu Rock Carvings, Dunhuang Mogao Caves, and Yungang Grottoes. The following conclusions can be made according to the quantitative results. Firstly, VGG-based method can be effectively applied to the gender recognition on non-Buddhist and Buddhist images. Compared with five classical feature extraction methods, VGG-based method performs not much better on non-Buddhist images, but superior on Buddhist images. Furthermore, the experiments are also carried out on three different training datasets, real-world facial datasets, including CUHK (CUHK is a student face database of Chinese University of Hong Kong). IMFDB (IMFDB is an Indian movie face database.) and CAS-PEAL (CAS-PEAL is a Chinese face database created by Chinese Academy of Sciences (CAS) with varying pose, expression, accessory, and lighting (PEAL). The unsatisfactory results based on IMFDB indicate that it is not valid to apply Indian facial images as a training set to the gender recognition on Buddhist image in China. With the sinicization of Buddhism, there were more Chinese rather than Indian characteristics on Buddhist images in ancient China. The results based on CAS-PEAL are more robust than those based on CUHK, as the former is mainly composed of mature adult faces, and the latter consists of young student faces. It gives the evidence that Buddha and Bodhisattva (Guanyin included) were as ideally mature men in original Buddhist art. The last but the most meaningful is that besides the time factor, the relationship between image making and the scriptures, or the intentional combination of male and female features, the geographical impact should not be ignored when we talk about the gender transformation of Guanyin in ancient China. The gender of Guanyin frescoes in Dunhuang Mogao Caves painted in the Sui, Tang, Five, Song and Yuan dynasties were always with prominent male characteristics (with tadpole-like moustache), while bodhisattvas in Yungang Grottoes engraved in the Northern Wei Dynasty were feminine even though they were made earlier than those in Dunhuang Mogao Caves. It is quite different from the common idea that the feminization of Guanyin occurred in the early Tang Dynasties and completely feminized in the late Tang Dynasty. Both the quantitative results and image analysis indicate that there might be a common model in a specific region, so the image-making of Guanyin was affected much more by geographical rather than temporal factor. In a word, it is quite a complicated issue for the feminization of Guanyin in China. |
| first_indexed | 2024-04-12T09:39:33Z |
| format | Article |
| id | doaj.art-f8a24662d657416aa4f63833fd09425e |
| institution | Directory Open Access Journal |
| issn | 2050-7445 |
| language | English |
| last_indexed | 2024-04-12T09:39:33Z |
| publishDate | 2022-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Heritage Science |
| spelling | doaj.art-f8a24662d657416aa4f63833fd09425e2022-12-22T03:38:08ZengSpringerOpenHeritage Science2050-74452022-06-0110111710.1186/s40494-022-00732-3Gender recognition of Guanyin in China based on VGGNetYongwen Huang0Dingding Chen1Haiyan Wang2Lulu Wang3School of Intelligent Technology and Engineering, Chongqing University of Science and TechnologyCollege of Computer Science, Chongqing UniversitySchool of Arts and Humanity, Sichuan Fine Arts InstituteCollege of Computer Science, Chongqing UniversityAbstract Gender transformation of Guanyin (Avalokitesvara in India) in China is an intrinsically fascinating research topic. Besides the inner source from the scriptures, literatures and epigraphs, iconological analysis is usually as the external evidence of Guanyin’s gender recognition. However, the ambiguous gender of the Guanyin image is often intentional and can be objectively assessed. Can computer vision be applied to the recognition objectively and quantitatively? In this paper, VGGNet (VGGNet is a very deep convolutional network for large-scale image recognition proposed by Visual Geometry Group of Oxford University) is applied to propose an automatic gender recognition system. To validate its efficiency, abundant experiments are implemented on the images of Dazu Rock Carvings, Dunhuang Mogao Caves, and Yungang Grottoes. The following conclusions can be made according to the quantitative results. Firstly, VGG-based method can be effectively applied to the gender recognition on non-Buddhist and Buddhist images. Compared with five classical feature extraction methods, VGG-based method performs not much better on non-Buddhist images, but superior on Buddhist images. Furthermore, the experiments are also carried out on three different training datasets, real-world facial datasets, including CUHK (CUHK is a student face database of Chinese University of Hong Kong). IMFDB (IMFDB is an Indian movie face database.) and CAS-PEAL (CAS-PEAL is a Chinese face database created by Chinese Academy of Sciences (CAS) with varying pose, expression, accessory, and lighting (PEAL). The unsatisfactory results based on IMFDB indicate that it is not valid to apply Indian facial images as a training set to the gender recognition on Buddhist image in China. With the sinicization of Buddhism, there were more Chinese rather than Indian characteristics on Buddhist images in ancient China. The results based on CAS-PEAL are more robust than those based on CUHK, as the former is mainly composed of mature adult faces, and the latter consists of young student faces. It gives the evidence that Buddha and Bodhisattva (Guanyin included) were as ideally mature men in original Buddhist art. The last but the most meaningful is that besides the time factor, the relationship between image making and the scriptures, or the intentional combination of male and female features, the geographical impact should not be ignored when we talk about the gender transformation of Guanyin in ancient China. The gender of Guanyin frescoes in Dunhuang Mogao Caves painted in the Sui, Tang, Five, Song and Yuan dynasties were always with prominent male characteristics (with tadpole-like moustache), while bodhisattvas in Yungang Grottoes engraved in the Northern Wei Dynasty were feminine even though they were made earlier than those in Dunhuang Mogao Caves. It is quite different from the common idea that the feminization of Guanyin occurred in the early Tang Dynasties and completely feminized in the late Tang Dynasty. Both the quantitative results and image analysis indicate that there might be a common model in a specific region, so the image-making of Guanyin was affected much more by geographical rather than temporal factor. In a word, it is quite a complicated issue for the feminization of Guanyin in China.https://doi.org/10.1186/s40494-022-00732-3GuanyinGender recognitionDeep convolutional neural networkBuddhist art |
| spellingShingle | Yongwen Huang Dingding Chen Haiyan Wang Lulu Wang Gender recognition of Guanyin in China based on VGGNet Heritage Science Guanyin Gender recognition Deep convolutional neural network Buddhist art |
| title | Gender recognition of Guanyin in China based on VGGNet |
| title_full | Gender recognition of Guanyin in China based on VGGNet |
| title_fullStr | Gender recognition of Guanyin in China based on VGGNet |
| title_full_unstemmed | Gender recognition of Guanyin in China based on VGGNet |
| title_short | Gender recognition of Guanyin in China based on VGGNet |
| title_sort | gender recognition of guanyin in china based on vggnet |
| topic | Guanyin Gender recognition Deep convolutional neural network Buddhist art |
| url | https://doi.org/10.1186/s40494-022-00732-3 |
| work_keys_str_mv | AT yongwenhuang genderrecognitionofguanyininchinabasedonvggnet AT dingdingchen genderrecognitionofguanyininchinabasedonvggnet AT haiyanwang genderrecognitionofguanyininchinabasedonvggnet AT luluwang genderrecognitionofguanyininchinabasedonvggnet |