Gender Prediction

AgeDB contains 16, 488 images of various famous people, such as actors/actresses, writers, scientists, politicians, etc. Every image is annotated with respect to the identity, age and gender attribute. There exist a total of 568 distinct subjects. The average number of images per subject is 29. The minimum and maximum age is 1 and 101, respectively. The average age range for each subject is 50.3 years. Source: AgeDB

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.

The dataset contains the annotations of characters' visual appearances, in the form of tracks of face bounding boxes, and the associations with characters' textual mentions, when available. The detection and annotation of the visual appearances of characters in each video clip of each movie was achieved through a semi-automatic approach. The released dataset contains more than 24k annotated video clips, including 63k visual tracks and 34k textual mentions, all associated with their character identities. Source: M-VAD Names Dataset

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.

InaGVAD is a Voice Activity Detection (VAD) and Speaker Gender Segmentation (SGS) dataset designed for representing the acoustic diversity of French TV and Radio programs. InaGVAD detailed description, together with a benchmark of 6 freely available VAD systems and 3 SGS systems, is provided in a paper presented in LREC-COLING 2024. InaGVAD contains 277 1-minute-long annotated recordings, partitioned into a 1h development and 3h37 test subset, allowing fair and reproducible system evaluation. Evaluation scripts provided with the corpus provide performance estimates in the same conditions as the 6 VAD and 3 SGS systems presented in the associated paper. Recordings were collected from 10 French radio and 18 TV channels categorized into 4 groups associated to diverse acoustic conditions : generalist radio, music radio, news TV, and generalist TV. InaGVAD provides an extended VAD and SGS annotation scheme, allowing to describe systems diverse abilities based on : * Speaker Traits categories ** 3 Genders : Female, Male, I Don't Know (IDK) ** 3 Age groups : Young (prepubescent), Adult, Ederly (Senior) ** 3 Speech Qualities : standard, interjections (ah, oh, eg, aie), atypical (crying, laughing or shouted speech, ill person voice, artificially distorted voices, vocal performance, monster voice...) *10 Non-Speech event categories : Applause, environmental noise, hubbub, jingle, foreground music, background music, respiration, non-intelligible laughers, other, empty The entire inaGVAD package; including corpus, annotations, evaluation scripts, and baseline training code; is made freely accessible, fostering future advancement in the domain.

This dataset comprises over 26,000 full names annotated with genders. Source: Gender Prediction Based on Vietnamese Names with Machine Learning Techniques