Efficientnet for human fer using transfer learning (Record no. 19045)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230327100148.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230327b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 13587 |
| Author | Singh, Rajesh |
| 245 ## - TITLE STATEMENT | |
| Title | Efficientnet for human fer using transfer learning |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.13(1), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 2792-2797p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Automatic facial expression recognition (FER) remained a<br/>challenging problem in computer vision. Recognition of human facial<br/>expression is difficult for machine learning techniques since there is a<br/>variation in emotional expression from person to person. With the<br/>advancement in deep learning and the easy availability of digital data,<br/>this process has become more accessible. We proposed an efficient<br/>facial expression recognition model based EfficientNet as backbone<br/>architecture and trained the proposed model using the transfer<br/>learning technique. In this work, we have trained the network on<br/>publicly available emotion datasets (RAF-DB, FER-2013, CK+). We<br/>also used two ways to compare our trained model: inner and cross-data<br/>comparisons. In an internal comparison, the model achieved an<br/>accuracy of 81.68 % on DFEW and 71.02 % on FER-2013. In a cross-<br/>data comparison, the model trained on RAF-DB and tested on CK+<br/>achieved 78.59%, while the model trained on RAF-DB and tested on<br/>FER-2013 achieved 56.10% accuracy. Finally, we generated an t-SEN<br/>distribution of our model on both datasets to demonstrate the model's<br/>inter-class discriminatory power. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 20278 |
| Co-Author | Sharma, Himanshu |
| 773 0# - HOST ITEM ENTRY | |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| Place, publisher, and date of publication | Chennai ICT Academy |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/IJSC_Vol_13_Iss_1_Paper_7_2792_2797.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 27/03/2023 | 2023-0516 | 27/03/2023 | 27/03/2023 | Articles Abstract Database |