Face Image Retrieval
4 papers with code • 0 benchmarks • 0 datasets
Face image retrieval is the task of retrieving faces similar to a query, according to the given criteria (e.g. identity) and rank them using their distances to the query.
( Image credit: CP-mtML )
Benchmarks
These leaderboards are used to track progress in Face Image Retrieval
Latest papers with no code
FaIRCoP: Facial Image Retrieval using Contrastive Personalization
Retrieving facial images from attributes plays a vital role in various systems such as face recognition and suspect identification.
R-Theta Local Neighborhood Pattern for Unconstrained Facial Image Recognition and Retrieval
RTLNP exploits relationships amongst the pixels in local neighborhood of the reference pixel at different angular and radial widths.
Similarity Guided Deep Face Image Retrieval
Face image retrieval, which searches for images of the same identity from the query input face image, is drawing more attention as the size of the image database increases rapidly.
Face Image Retrieval With Attribute Manipulation
For example, a user can ask for retrieving images similar to a query image, but with a different hair color, and no preference for absence/presence of eyeglasses in the results.
From A Glance to "Gotcha": Interactive Facial Image Retrieval with Progressive Relevance Feedback
Facial image retrieval plays a significant role in forensic investigations where an untrained witness tries to identify a suspect from a massive pool of images.
Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval
The DNDCMH network consists of two separatecomponents: an attribute-based deep cross-modal hashing (ADCMH) module, which uses a margin (m)-based loss function toefficiently learn compact binary codes to preserve similarity between modalities in the Hamming space, and a neural error correctingdecoder (NECD), which is an error correcting decoder implemented with a neural network.
Using Deep Cross Modal Hashing and Error Correcting Codes for Improving the Efficiency of Attribute Guided Facial Image Retrieval
With benefits of fast query speed and low storage cost, hashing-based image retrieval approaches have garnered considerable attention from the research community.
Deep Face Image Retrieval: a Comparative Study with Dictionary Learning
The comparative results of the experiments conducted on three standard face image datasets show that the best performers for face image retrieval are Alexlayer7 with $K$-means and SSF, Alexlayer6 with $K$-SVD and SSF, and Alexlayer6 with $K$-means and SSF.
CP-mtML: Coupled Projection multi-task Metric Learning for Large Scale Face Retrieval
The experiments clearly demonstrate the scalability and improved performance of the proposed method on the tasks of identity and age based face image retrieval compared to competitive existing methods, on the standard datasets and with the presence of a million distractor face images.
Two Birds, One Stone: Jointly Learning Binary Code for Large-Scale Face Image Retrieval and Attributes Prediction
In this way, the learned binary codes can be applied to not only fine-grained face image retrieval, but also facial attributes prediction, which is the very innovation of this work, just like killing two birds with one stone.
