Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-View Exclusive Unsupervised Dimension Reduction for Video-Based Facial Expression Recognition
Authors: Liping Xie, Dacheng Tao, Haikun Wei
IJCAI 2016 | Venue PDF | 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}. |