Multi-View Exclusive Unsupervised Dimension Reduction for Video-Based Facial Expression Recognition
Authors: Liping Xie, Dacheng Tao, Haikun Wei
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two challenging video-based FER datasets demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, China QCIS and FEIT, University of Technology Sydney, Australia |
| Pseudocode | Yes | Algorithm 1 The efficient iterative algorithm for solving Eq.(6) |
| Open Source Code | No | No explicit statement about providing open-source code for the described methodology was found. |
| Open Datasets | Yes | The first dataset is the facial expression (FE) dataset proposed in [Doll ar et al., 2005], which we call FE05 in this paper. The second FE dataset is the Oulu-CASIA VIS (CASIA for short) database [Li et al., 2013]. |
| Dataset Splits | Yes | The five-fold cross-validation strategy is adopted for both datasets for tuning the parameters. We randomly separate these subjects into two groups: 70 subjects with six expressions (420 samples) for training, 10 subjects (60 samples) for testing. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instances) used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions classifiers like SVM and kNN, but does not provide specific version numbers for any software, libraries, or programming languages used. |
| Experiment Setup | Yes | The five-fold cross-validation strategy is adopted for both datasets for tuning the parameters. The candidate set for both trade-off parameters γ and is {10i|i = 5, 4, ..., 3, 4}. Accuracy is averaged over five runs for each dimension r in {1, 2, 5, 8, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300}. |