Context

Recently, many applications from biometrics,to entertainment use the information extracted from face images that contain information about age, gender, ethnic background, and emotional state. Automatic age estimation from facial images is one of the popular and challenging tasks that have different fields of applications such as controlling the content of the watched media depending on the customer's age.
So facial feature analysis has been a topic of interest mainly due to its applicability and Deep Learning techniques are now making it possible for face analysis to be not just a dream but a reality. This simple practice dataset can get you more acquainted with application of deep learning in age detection.

Content

Indian Movie Face database (IMFDB) is a large unconstrained face database consisting of 34512 images of 100 Indian actors collected from more than 100 videos. All the images are manually selected and cropped from the video frames resulting in a high degree of variability interms of scale, pose, expression, illumination, age, resolution, occlusion, and makeup. IMFDB is the first face database that provides a detailed annotation of every image in terms of age, pose, gender, expression and type of occlusion that may help other face related applications.

The dataset provided a total of 19906 images.The attributes of data are as follows:

• ID – Unique ID of image
• Class – Age bin of person in image

image ref : Automatic age estimation based on CNN

Acknowledgements

CVIT focuses on basic and advanced research in image processing, computer vision, computer graphics and machine learning. This center deals with the generation, processing, and understanding of primarily visual data as well as with the techniques and tools required doing so efficiently. The activity of this center overlaps the traditional areas of Computer Vision, Image Processing, Computer Graphics, Pattern Recognition and Machine Learning. CVIT works on both theoretical as well as practical aspects of visual information processing. Center aims to keep the right balance between the cutting edge academic research and impactful applied research.

Inspiration

The main task is to predict the age of a person from his or her facial attributes. For simplicity, the problem has been converted to a multiclass problem with classes as Young, Middle and Old.

Version 2: Faces dataset for regression added

UTKFace dataset is a large-scale face dataset with long age span (range from 0 to 116 years old). The dataset consists of over 20,000 face images with annotations of age, gender, and ethnicity. The images cover large variation in pose, facial expression, illumination, occlusion, resolution, etc. This dataset could be used on a variety of tasks, e.g., face detection, age estimation, age progression/regression, landmark localization, etc. Some sample images are shown as following:

• consists of 20k+ face images in the wild (only single face in one image)
• provides the correspondingly aligned and cropped faces
• provides the corresponding landmarks (68 points)
• images are labelled by age, gender, and ethnicity

Complete Dataset: https://susanqq.github.io/UTKFace/

The labels of each face image is embedded in the file name, formated like [age][gender][race]_[date&time].jpg

• [age] is an integer from 0 to 116, indicating the age
• [gender] is either 0 (male) or 1 (female)
• [race] is an integer from 0 to 4, denoting White, Black, Asian, Indian, and Others (like Hispanic, Latino, Middle Eastern).
• [date&time] is in the format of yyyymmddHHMMSSFFF, showing the date and time an image was collected to UTKFace