Predicting a person's gender based on the iris texture has been explored by several researchers. This paper considers several dimensions of experimental work on this problem, including person-disjoint train and test, and the effect of cosmetics on eyelash occlusion and imperfect segmentation. We also consider the use of multi-layer perceptron and convolutional neural networks as classifiers, comparing the use of data-driven and hand-crafted features. Our results suggest that the gender-from-iris problem is more difficult than has so far been appreciated. Estimating accuracy using a mean of N person-disjoint train and test partitions, and considering the effect of makeup - a combination of experimental conditions not present in any previous work - we find a much weaker ability to predict gender-from-iris texture than has been suggested in previous work.
Later, @cite_1 used a Support Vector Machine (SVM) classifier with features extracted using spot and line detectors and Law's texture measures. They used a dataset of 600 images and a cross-validation protocol with 2, 5 and 10 folds, with person-disjoint partitions. They considered both race-from-iris and gender-from-iris, and their classification accuracy on gender-from-iris ranged from @math 83 In the work of @cite_11 , using an SVM to classify Local Binary Pattern (LBP) features extracted from 3,000 iris images yielded an accuracy of @math 80 20 @math train test @math 72 28 @math 60
@cite_1 @cite_11
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We used both inner and outer face cues.External cues improve classification performance for gender recognition.FIS framework improves classification results when combined with SVM.Unconstrained databases provide better results than that of constrained databases.We obtained 93.35 accuracy on Groups LFW cross-database test. In this paper, we propose a novel gender recognition framework based on a fuzzy inference system (FIS). Our main objective is to study the gain brought by FIS in presence of various visual sensors (e.g., hair, mustache, inner face). We use inner and outer facial features to extract input variables. First, we define the fuzzy statements and then we generate a knowledge base composed of a set of rules over the linguistic variables including hair volume, mustache and a vision-sensor. Hair volume and mustache information are obtained from Part Labels subset of Labeled Faces in the Wild (LFW) database and vision-sensor is obtained from a pixel-intensity based SVM+RBF classifier trained on different databases including Feret, Groups and GENKI-4K. Cross-database test experiments on LFW database showed that the proposed method provides better accuracy than optimized SVM+RBF only classification. We also showed that FIS increases the inter-class variability by decreasing false negatives (FN) and false positives (FP) using expert knowledge. Our experimental results yield an average accuracy of 93.35 using Groups LFW test, while the SVM performance baseline yields 91.25 accuracy. This paper presents a thorough study of gender classification methodologies performing on neutral, expressive and partially occluded faces, when they are used in all possible arrangements of training and testing roles. A comprehensive comparison of two representation approaches (global and local), three types of features (grey levels, PCA and LBP), three classifiers (1-NN, PCA+LDA and SVM) and two performance measures (CCR and d') is provided over single- and cross-database experiments. Experiments revealed some interesting findings, which were supported by three non-parametric statistical tests: when training and test sets contain different types of faces, local models using the 1-NN rule outperform global approaches, even those using SVM classifiers; however, with the same type of faces, even if the acquisition conditions are diverse, the statistical tests could not reject the null hypothesis of equal performance of global SVMs and local 1-NNs.