Improving Facial Expression Recognition Using HOG with SVM and Modified Datasets Classified by Alexnet
Bibliographic record
Abstract
Facial expressions are one of the communication ways between humans and their behavior can be determined through their facial expressions.Recently computer technology has been used to identify the facial expressions of people in order to predict their intentions.It remains a challenge for facial expression recognition to extract discriminative features from training sets with few labels because most deep learning-based algorithms primarily rely on spatial information and large labels.In this paper, we propose a system to classify seven types of facial expressions (Angry, Sadness, Surprise, Happiness, Fear, Neutral, and Disgust) instead of six, as in most previous research.In the proposed system, machine learning algorithms with deep learning are used to increase classification accuracy based on removing some unimportant facial regions.The support vector machine (SVM) algorithm is trained to detect the eyes and mouth regions from the face depending on histogram-oriented gradient (HOG) which is used as a features extractor.Then, merge the eyes and mouth regions for each image to create a new form of an image.After that, five different types of images are generated from the merged image named (RGB, HSV, Gray, Binary, and YCbCr).The images are fed one by one into the convolution neural network (CNN) algorithm.Finally, the voting process is used to select the most predictive class.The proposed system has been tested on three different types of datasets (KDEF, JAFFE, and FER2013) and the prediction accuracy in the system has reached more than 98% in all used datasets.The conclusion is that eliminating unimportant regions impacts the results of the classification accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".