IEEE Signal Processing Magazine - January 2018 - 74

the performance of face recognition. It was also found that
good alignment can greatly help face recognition performance.

Performance summary
We summarize the performance results of recent face identification and verification algorithms for the well-known LFW
[81] and IJB-A [69] data sets.

LFW data set
The LFW data set contains 13,233 face images of 5,749 subjects
where 1,680 subjects have two or more images. We show the performance comparisons using the standard protocol of face verification which defines 3,000 positive pairs and 3,000 negative pairs
in total and further splits them into ten disjoint subsets for cross
validation. Each subset contains 300 positive and 300 negative
pairs. It contains 7,701 images of 4,281 subjects. In Table 2, we
show the mean accuracy of state-of-the-art deep-learning-based
methods: DeepFace [58], DeepID2 [61], DeepID3 [86], FaceNet
[62], Yi et al. [13], Wang et al. [87], Ding et al. [88], Parkhi et al.
[17], Wen et al. [66], Liu et al. [67], Ranjan et al. [68], and human
performance on the "funneled" LFW images.

IJB-A benchmark a data set
Besides the LFW data set, we also present the performance comparisons for the challenging IJB-A data set. The IJB-A data set
contains 500 subjects with 5,397 images and 2,042 videos splitting into 20,412 frames. Sample images and video frames from
the data sets are shown in Figure 6. The ROC curve measures

the performance in the verification scenarios, and the cumulative match characteristic (CMC) score measures the accuracy in
closed set identification scenarios. In addition, the IJB-A evaluation protocol consists of face verification (1:1 matching) over
ten splits. Each split contains approximately 11,748 pairs of templates (1,756 positive and 9,992 negative pairs) on average. Similarly, the face identification (1:N search) protocol also consists
of ten splits, which are used to evaluate the search performance.
In each search split, there are about 112 gallery templates and
1,763 probe templates (i.e., 1,187 genuine probe templates and
576 impostor probe templates). The training set contains 333
subjects, and the test set contains 167 subjects without any overlapping subjects. Ten random splits of training and testing are
provided. Unlike the LFW [81] and YTF [89] data sets, which
only use a sparse set of negative pairs to evaluate the verification
performance, the IJB-A data set divides the images/video frames
into gallery and probe sets so that all the available positive and
negative pairs are used for the evaluation. Also, each gallery and
probe set consist of multiple templates. Each template contains a
combination of images or frames sampled from multiple image
sets or videos of a subject. In contrast to the LFW and YTF data
sets, which only include faces detected by the Viola Jones face
detector [90], the images in the IJB-A contain extreme pose, illumination, and expression variations. These factors essentially make
the IJB-A a challenging face recognition data set.
The CMC scores and the ROC are used to evaluate the performance of different algorithms for face identification/verification.
The identification/verification results for the IJB-A data set are

Table 2. The accuracy of different verification methods on the LFW data set.

Method

Number of
Networks

Training Set

Metric

Mean Accuracy ± Std

DeepFace [58]

One

4.4 million images of 4,030 subjects, private

Cosine

95.92% ± 0.29%

DeepFace

Seven

4.4 million images of 4,030 subjects, private

Unrestricted, SVM

97.35% ± 0.25%

DeepID2 [61]

One

202,595 images of 10,117 subjects, private

Unrestricted, JB

95.43%

DeepID2

202,595 images of 10,117 subjects, private

Unrestricted, JB

99.15% ± 0.15%

DeepID3 [86]

202,595 images of 10,117 subjects, private

Unrestricted, JB

99.53% ± 0.10%

FaceNet [62]

One

260 million images of 8 million subjects, private

99.63% ± 0.09%

Yi et al. [13]

One

494,414 images of 10,575 subjects, public

Cosine

96.13% ± 0.30%

Yi et al.

One

494,414 images of 10,575 subjects, public

Unrestricted, JB

97.73% ± 0.31%

Wang et al. [87]

One

494,414 images of 10,575 subjects, public

Cosine

96.95% ± 1.02%

Wang et al.

Seven

494,414 images of 10,575 subjects, public

Cosine

97.52% ± 0.76%

Wang et al.

One

494,414 images of 10,575 subjects, public

Unrestricted, JB

97.45% ± 0.99%

Wang et al.

Seven

494,414 images of 10,575 subjects, public

Unrestricted, JB

98.23% ± 0.68%

Ding et al. [88]

Eight

471,592 images of 9,000 subjects, public

Unrestricted, JB

99.02% ± 0.19%

Parkhi et al. [17]

One

2.6 million images of 2,622 subjects, public

Unrestricted, TDE

98.95%

Wen et al. [66]

One

0.7 million images of 17,189 subjects, public

Cosine

99.28%

Liu et al. [67]

One

494,414 images of 10,575 subjects, public

Cosine

99.42%

Ranjan et al. [68]

One

3.7 million images of 58,207 subjects, public

Cosine

99.78%

Human, funneled [87]

N/A

99.20%

IEEE SIGNAL PROCESSING MAGAZINE

January 2018

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