Indoor Facial 75 Expressions Dataset

Home » Case Study » Indoor Facial 75 Expressions Dataset

Project Overview:

Objective

Our recent project, the Innovative Facial Expression Dataset, stands as a testament to our expertise in data collection and annotation. With meticulous care, we compiled a comprehensive dataset of 75 unique facial expressions, captured under controlled indoor lighting. This dataset serves as a cornerstone in propelling forward the fields of facial recognition technology, emotion detection AI, animation, virtual avatars, and psychological research. Moreover, it provides invaluable insights into the intricacies of human emotion and expression, fostering advancements in various interdisciplinary domains.

Scope

Our team actively gathered a diverse collection of facial images, showcasing a broad spectrum of expressions. Moreover, we focused on capturing the subtleties and nuances of facial movements, ensuring the dataset’s relevance across various industries.

Sources

In studio settings, where controlled lighting is employed, it is essential to capture consistent facial details. By utilizing strategic lighting setups, photographers can ensure uniform illumination across the subject’s face. This, in turn, enables the camera to capture intricate facial features consistently. Additionally, employing active voice in directing the lighting allows for precise adjustments to highlight specific areas of interest on the subject’s face.
To ensure diversity, include participants from varied ethnicities, age groups, and genders. Additionally, actively incorporate transition words throughout the content.
Subjects were provided with guidance to accurately achieve each of the 75 expressions. First, they were instructed to carefully study each expression, paying close attention to its specific details. Then, they were encouraged to practice mimicking the expressions in front of a mirror, ensuring they understood how to replicate each facial movement precisely. Next, participants were advised to gradually incorporate the expressions into their daily interactions, focusing on using them in appropriate contexts. Additionally, they were reminded to seek feedback from peers or instructors to fine-tune their execution of the expressions.
Zooming in with precision, ensuring that every facial detail, including minor muscle movements, is evident, we capture close-up shots.

Data Collection Metrics

Environment: In studio settings, we actively utilized controlled lighting to consistently capture detail.
Diversity: In order to ensure representation from different ethnicities, age groups, and genders, we actively sought participants from every major continent. Additionally, we made sure to include individuals from diverse
Volume: We have successfully gathered 385,000 images, each comprising 5,000 images per expression. Additionally, we’ve included more transition words to enhance the flow of the content.
Participant Range: This content actively engaged 5,200 unique individuals, each actively demonstrating all 75 expressions. Furthermore, it actively fostered participation by incorporating a variety of interactive elements. Additionally, it actively facilitated seamless communication through the utilization of various communication channels. Moreover, it actively encouraged dynamic interaction by providing opportunities for real-time feedback and discussion.
Age Diversity: The study included participants ranging in age from 5 to 80 years old. Additionally, it encompassed individuals from diverse age groups, incorporating children as young as 5 and adults up to the age of 80.

Annotation Process

Stages

Initial Refinement: We eliminated any images with blurriness or inconsistent lighting.
Expression Labeling: Each image was carefully labeled with its corresponding expression.
Muscle Movement Detailing: For specific expressions, we annotated the engaged facial muscles.
Quality Control: Conducted rigorous reviews to ensure accuracy and consistency in annotations.

Annotation Metrics

Total Annotations: 385,000 expression labels and 154,000 muscle movement annotations.
Quality Assurance: Reviewed 20% of the dataset (77,000 images) for annotation accuracy.

Quality Assurance

Stages

Expert Review: We involved facial expression experts, animators, and psychologists in evaluating the dataset.
Automated checks: Additionally, as part of our automated checks, we employed software solutions to verify consistency.
Annotation Uniformity: We conducted multiple annotator reviews to ensure uniform labeling.

QA Metrics

Annotations Evaluated by Experts: 75,000 (20% of total annotations)
Inconsistencies Detected and Amended: 7,500 (2% of total annotations)

Conclusion

Our Indoor Facial 75 Expressions Dataset represents a pioneering achievement in the field of emotion and facial recognition research. Through our meticulous approach to data collection and annotation, we have positioned this dataset to revolutionize industries from entertainment to healthcare. This reflects our company’s commitment to providing diverse and detailed datasets for machine learning models.