2008/12/29 by Philipp Wolfrum, Christian Wolff, Jörg Lücke +1 · 1 citation
Neuroscience · Computer Science · #Face Recognition and Perception #Face and Expression Recognition #Neural dynamics and brain function
paper · doi:10.1167/8.7.34
openalex publication_date 2008/12/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22
Our aim here is to create a fully neural, functionally competitive, and correspondence-based model for invariant face recognition. By recurrently integrating information about feature similarities, spatial feature relations, and facial structure stored in memory, the system evaluates face identity ("what"-information) and face position ("where"-information) using explicit representations for both. The network consists of three functional layers of processing, (1) an input layer for image representation, (2) a middle layer for recurrent information integration, and (3) a gallery layer for memory storage. Each layer consists of cortical columns as functional building blocks that are modeled in accordance with recent experimental findings. In numerical simulations we apply the system to standard benchmark databases for face recognition. We find that recognition rates of our biologically inspired approach lie in the same range as recognition rates of recent and purely functionally motivated systems.