2 research outputs found
Training-Free Synthesized Face Sketch Recognition Using Image Quality Assessment Metrics
Face sketch synthesis has wide applications ranging from digital
entertainments to law enforcements. Objective image quality assessment scores
and face recognition accuracy are two mainly used tools to evaluate the
synthesis performance. In this paper, we proposed a synthesized face sketch
recognition framework based on full-reference image quality assessment metrics.
Synthesized sketches generated from four state-of-the-art methods are utilized
to test the performance of the proposed recognition framework. For the image
quality assessment metrics, we employed the classical structured similarity
index metric and other three prevalent metrics: visual information fidelity,
feature similarity index metric and gradient magnitude similarity deviation.
Extensive experiments compared with baseline methods illustrate the
effectiveness of the proposed synthesized face sketch recognition framework.
Data and implementation code in this paper are available online at
www.ihitworld.com/WNN/IQA_Sketch.zip
Deep Embedding using Bayesian Risk Minimization with Application to Sketch Recognition
In this paper, we address the problem of hand-drawn sketch recognition.
Inspired by the Bayesian decision theory, we present a deep metric learning
loss with the objective to minimize the Bayesian risk of misclassification. We
estimate this risk for every mini-batch during training, and learn robust deep
embeddings by backpropagating it to a deep neural network in an end-to-end
trainable paradigm. Our learnt embeddings are discriminative and robust despite
of intra-class variations and inter-class similarities naturally present in
hand-drawn sketch images. Outperforming the state of the art on sketch
recognition, our method achieves 82.2% and 88.7% on TU-Berlin-250 and
TU-Berlin-160 benchmarks respectively.Comment: Accepted at ACCV 201