Pose-Robust Face Recognition via Deep Residual Equivariant Mapping

Face recognition achieves exceptional success thanks to the emergence of deep learning. However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces... (read more)

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Datasets


Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Face Identification IJB-A Deep Residual Equivariant Mapping Accuracy 94.60% # 1
Face Verification IJB-A Deep Residual Equivariant Mapping TAR @ FAR=0.01 94.40% # 6

Methods used in the Paper


METHOD TYPE
Average Pooling
Pooling Operations
ReLU
Activation Functions
1x1 Convolution
Convolutions
Batch Normalization
Normalization
Bottleneck Residual Block
Skip Connection Blocks
Global Average Pooling
Pooling Operations
Residual Block
Skip Connection Blocks
Kaiming Initialization
Initialization
Max Pooling
Pooling Operations
Residual Connection
Skip Connections
Convolution
Convolutions
ResNet
Convolutional Neural Networks