Age And Gender Classification

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CI-MNIST (Correlated and Imbalanced MNIST) is a variant of MNIST dataset with introduced different types of correlations between attributes, dataset features, and an artificial eligibility criterion. For an input image $x$, the label $y \in \\{1, 0\\}$ indicates eligibility or ineligibility, respectively, given that $x$ is even or odd. The dataset defines the background colors as the protected or sensitive attribute $s \in \\{0, 1\\}$, where blue denotes the unprivileged group and red denotes the privileged group. The dataset was designed in order to evaluate bias-mitigation approaches in challenging setups and be capable of controlling different dataset configurations.

We have cleaned the noisy IMDB-WIKI dataset using a constrained clustering method, resulting this new benchmark for in-the-wild age estimation. The annotations also allow this dataset to use for some other tasks, like gender classification and face recognition/verification. For more details, please refer to our FPAge paper.

The LAGENDA dataset is a large-scale dataset with age and gender annotations for face and body bounding boxes. The dataset consists of 67,159 images from the Open Images Dataset and comprises 84,192 pairs (FaceCrop, BodyCrop). This dataset offers a high level of diversity, encompassing various scenes and domains. It contains minimal celebrity data, thus reflecting real-world, in-the-wild scenarios. The dataset spans a wide age range, from 0 to 95 years old.

EEG signals from 60 users have been recorded whose age range lies between 6 and 55 years. Among all, there were 25 females and 35 male users. In general, all the participants were either school children or belonged to the socioeconomic cross section of the population with no medical history. The EEG recordings were acquired from all 14 electrodes operating at a sampling rate of 128 Hz. During recording, the participants were asked to comfortably sit on the chair with clear thoughts and a relaxed state.

Although research on author profiling has quite progressed in abundant resources languages, it is still infancy for limited resources languages such as Bengali. This repository contains our Bangla Author Profiling Dataset (BN-AuthProf). The primary objective is to introduce and benchmark the performance of machine learning approaches on Age and Gender Classification tasks from the social media status of people. BN-AuthProf dataset consists of 300 anonymized authors and 30,131 manually curated Facebook posts in Bangla, tagged with age and gender information.