Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition
Authors: Shizhong Han, Zibo Meng, AHMED-SHEHAB KHAN, Yan Tong
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on four benchmark AU databases have demonstrated that the IB-CNN yields significant improvement over the traditional CNN and the boosting CNN without incremental learning, as well as outperforming the state-of-the-art CNN-based methods in AU recognition. |
| Researcher Affiliation | Academia | Department of Computer Science & Engineering, University of South Carolina, Columbia, SC {han38, mengz, akhan}@email.sc.edu, tongy@cse.sc.edu |
| Pseudocode | Yes | Algorithm 1 Incremental Boosting Algorithm for the IB-CNN |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for their method or provide a link to a code repository. |
| Open Datasets | Yes | Experimental results on four benchmark AU-coded databases, i.e., Cohn-Kanade (CK) [25] databse, FERA2015 SEMAINE database [11], FERA2015 BP4D database [11], and Denver Intensity of Spontaneous Facial Action (DISFA) database [12] |
| Dataset Splits | Yes | All the models compared were trained on the training set and evaluated on the validation set. The training-testing process was repeated 5 times. The mean and standard deviation of F1 score and two-alternative forced choice (2AFC) score are calculated from the 5 runs for each target AU. ... A 9-fold cross-validation strategy is employed for the DISFA database, where 8 subsets of 24 subjects were utilized for training and the remaining one subset of 3 subjects for testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for the experiments. |
| Software Dependencies | No | The proposed IB-CNN is implemented based on a modification of cifar10_quick in Caffe [28]. (No version number for Caffe or other libraries is provided). |
| Experiment Setup | Yes | The stochastic gradient descent, with a momentum of 0.9 and a mini-batch size of 100, is used for training the CNN for each target AU. |