A survey of college students in the United States in 2023-2024 found that around 38 percent had symptoms of depression. Symptoms of depression vary in severity and can include a loss of interest/pleasure in things once found enjoyable, feelings of sadness and hopelessness, fatigue, changes in sleep, and thoughts of death or suicide. Mental health among college students Due to the life changes and stress that often come with attending college, mental health problems are not unusual among college students. The most common mental health problems college students have been diagnosed with are anxiety disorders and depression. Fortunately, these are two of the most treatable forms of mental illness, with psychotherapy and/or medications the most frequent means of treatment. However, barriers to access mental health services persist, with around 22 percent of college students stating that in the past year financial reasons caused them to receive fewer services for their mental or emotional health than they would have otherwise received. Depression in the United States Depression is not only a problem among college students but affects people of all ages. In 2021, around ten percent of those aged 26 to 49 years in the United States reported a major depressive episode in the past year. Depression in the United States is more prevalent among females than males, but suicide is almost four times more common among males than females. Death rates due to suicide in the U.S. have increased for both genders in the past few years, highlighting the issue of depression and other mental health disorders and the need for easy access to mental health services.

Introduction

Welcome to the Native American Facial Expression Image Dataset, curated to support the development of advanced facial expression recognition systems, biometric identification models, KYC verification processes, and a wide range of facial analysis applications. This dataset is ideal for training robust emotion-aware AI solutions.

Facial Expression Data

The dataset includes over 1000 high-quality facial expression images, grouped into participant-wise sets. Each participant contributes:

Diversity & Representation

Image Quality & Capture Conditions

To ensure generalizability and robustness in model training, images were captured under varied real-world conditions:

Metadata

Each participant's image set is accompanied by detailed metadata, enabling precise filtering and training:

This metadata helps in building expression recognition models that are both accurate and inclusive.

Use Cases & Applications

This dataset is ideal for a variety of AI and computer vision applications, including:

Secure & Ethical Collection

Dataset Updates & Customization

To support evolving AI development needs, this dataset is regularly updated and can be tailored to project-specific requirements. Custom options include:

Title:

Spanish Anorexia Dataset

  Dataset Description

Paper: Detecting Anorexia in {S}panish Tweets Point of Contact: plubeda@ujaen.es, flor.plaza@unibocconi.it Mental health is one of the main concerns of today’s society. Early detection of symptoms can greatly help people with mental disorders. People are using social networks more and more to express emotions, sentiments and mental states. Thus, the treatment of this information using NLP technologies can be applied to… See the full description on the dataset page: https://huggingface.co/datasets/SINAI/SAD.