Gender Classification in Human Face Images for Smart Phone Applications Based on Local Texture Information and Evaluated Kullback-Leibler Divergence
Bibliographic record
Abstract
One of the main steps in human identification is gender classification which can increase the identification accuracy.In many smart phone applications, human identification plays an important role in different reasons such as login permission, sign up certificates, etc.So, accurate gender classification algorithms may increase the accuracy of smart phone applications and reduce its complexity.Also, one of the benefits of gender classification algorithms is for parents to monitor the social network contacts of their child in terms of gender.Different methods have been proposed to do it accurately so far.In all methods, classification accuracy is the main challenge for researchers.But, in smart phone applications, some challenges such as rotation, gray scale variations may reduce the accuracy.In this respect, a rotation invariant approach is proposed in this paper to classify genders in human face images based on improved version of local binary patters (ILBP).Local binary pattern (LBP) is a texture descriptor, which extract local contrast and spatial structure information.Some issues such as noise sensitivity, rotation sensitivity and low discriminative features can be considered as disadvantages of the basic LBP.ILBP solves the above disadvantages using a new theory for binary patterns categorization.The proposed approach includes two stages.First of all, a feature vector is extracted for human face images based on ILBP.Next, Kullback-Leibler divergence classifier is used to classify gender.In this paper Kullback Leibler classifier is evaluated based on log likelihood ratio as distance measure.In the result part, two databases, self-collected and ICPR are used as human face database.Results are compared by different well known methods in this literature that shows the high quality of the proposed approach in terms of accuracy rate.Other main advantages of our approach are rotation invariant, low noise sensitivity, size invariant and low computational complexity.The proposed approach decreases the computational complexity of smartphone applications because of reducing the number of database comparisons.It can also improve performance of the synchronous applications in the smartphones because of memory and CPU usage reduction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".