Welcome to RPI Intelligent Systems Lab (ISL) Image Databases

Overview of RPI ISL Image Databases

 

The goal of the ISL Image Databases is to share image data sets with researchers around the world. The ISL image databases consist of four major parts: medical image databases, fatigue image databases, face and eye databases, and facial expression databases. 

 

The RPI ISL Image Databases

 

A. Face and Eye Databases

1.  Unprocessed images

This database contains the unprocessed images from different sources.

 

o      ISL WebImage Database

This database is collected from website by our own research group. It can be used in many applications such as face detection, eye detection, and face pose estimation.

o      ISL Face Database

This database is created by our own research group. It consists of video sequences and static images. It can be used in many applications such as face and eye detection, face tracking, face pose estimation, and face recognition.

o      ISL Eye Image Database

This database is constructed by our own research group. It consists of image sequences taken under IR cameras. Each image sequence is decomposed into dark pupil image sequence and its corresponding bright pupil image sequence. This database is primarily used in eye detection and tracking.

 

o      Face Databases from Other Research Groups

 

o      Eye Databases from Other Research Groups

 

 

1.  Training Image Databases

The training image databases contain the cropped image region of interest (ROI) for training various classifiers including eye, eye corner, and face.

 

 

B. Facial Expression databases

o      ISL Facial Expression Databases

This database is collected by our own research group. It is primarily used for facial action unit recognition. It consists of two subsets: frontal-view facial expression database and multi-view facial expression database.

 

o      ISL Enhanced Cohn-Kanade AU-coded Facial Expression Database

Our research group has manually created more groundtruth data for Cohn-Kanade AU-coded facial expression database including the frame-by-frame AU labeling and coordinates of 34 facial feature points.

 

C. Human Fatigue Database

This database consists of face and eye images for human subjects under different fatigue states

 

D. Medical Image Databases

This database consists of medical images for microtubule segmentation, membrane segmentation, and C-elegans (nematode worms) segmentation.

 

Others' Image Databases

o      Face Databases from Other Research Groups

o      Eye Databases from Other Research Groups

o      Facial Expression Databases from Other Research Groups

 

Contact Information

 

The RPI ISL databases are publicly available for non-commercial use. Any commercial distribution or any act related to commercial use of this database is strictly prohibited. For access to RPI ISL databases, please contact Professor Qiang Ji (qji@ecse.rpi.edu), if you are interested in getting a copy of the databases.