1 

Comparing human and machine face recognition 

Face Recognition Algorithms 

Surpass Humans 

Alice J. O’TOOLE, P. Jonathon PHILLIPS, Fang JIANG, Janet AYYAD, Nils PENARD, 

and Hervé ABDI* 

Abstract—There has been significant progress in improving the performance of computer-based face recognition 

algorithms over the last decade. Although algorithms have been tested and compared extensively with each 

other, there has been virtually no work comparing the accuracy of computer-based face recognition systems 

with humans. We compared seven state-of-the-art face recognition algorithms with humans on a face-matching 

task. Humans and algorithms determined whether pairs of face images, taken under different illumination 

conditions, were pictures of the same person or of different people. Three algorithms surpassed human 

performance matching face pairs prescreened to be “difficult” and six algorithms surpassed humans on “easy” 

face pairs. Although illumination variation continues to challenge face recognition algorithms, current 

algorithms compete favorably with humans. The superior performance of the best algorithms over humans, in 

light of the absolute performance levels of the algorithms, underscores the need to compare algorithms with the 

best current control—humans. 

Index Terms—face and gesture recognition, performance evaluation of algorithms and systems, human 

information processing 

I. INTRODUCTION 

An increase in security concerns worldwide has focused public attention on the accuracy 

of computer-based face recognition systems for security applications. How accurate must 

a face recognition algorithm be to contribute to these applications? Over the last decade, 

academic computer vision researchers and commercial product developers have 

improved the performance of automated face recognition algorithms on a variety of 

Manuscript received March 14, 2006. This work was supported by a contract to A. J. O’Toole and H. Abdi from TSWG. P. J. Phillips 

was supported in part by funding from the National Institute of Justice. 

A. J. O’Toole is at the School of Behavioral and Brain Sciences, GR4.1 The University of Texas at Dallas Richardson, TX 75083-

0688, USA, (e-mail: otoole@utdallas.edu). 

P.J. Phillips is with the National Institute of Standards and Technology, 100 Bureau Dr., MS 8940 Gaithersburg MD 20899 

jonathon@nist.gov 

Fang Jinag is at the School of Behavioral and Brain Sciences, GR4.1 The University of Texas at Dallas Richardson, TX 75083-0688, 

USA, (e-mail: fxj018100@utdallas.edu). 

Janet Ayyad is at the School of Behavioral and Brain Sciences, GR4.1 The University of Texas at Dallas Richardson, TX 75083-0688, 

USA. 

Nils Pénard is at the School of Behavioral and Brain Sciences, GR4.1 The University of Texas at Dallas Richardson, TX 75083-0688, 

USA, (e-mail: npenard@utdallas.edu). 

Hervé Abdi is at the School of Behavioral and Brain Sciences, GR4.1 The University of Texas at Dallas Richardson, TX 75083-0688, 

USA, (e-mail: herve@utdallas.edu). 
 

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Comparing human and machine face recognition 

challenging face recognition tasks. Information about the performance of automated face 

recognition systems is available to the public in the form of scholarly journal articles (for 

a review, see [1]), commercially disseminated product information, and United States 

Government sponsored evaluations (e.g., [2]-[5]). Virtually no information is available, 

however, about the accuracy of face recognition algorithms relative to humans. Because 

humans currently perform face recognition tasks in most real world security situations, it 

is unclear whether the use of algorithms improves security or puts it at greater risk. 

Notwithstanding the issue of human performance, it is difficult even to make direct 

comparisons among algorithms in a way that gives an accurate idea of the current state of 

the art [6]. This is because most journal articles and product pamphlets consider only one 

or two systems at a time and report results that are based on facial image sets that vary 

widely in number and quality. United States Government-funded competitions allow for 

direct comparisons among multiple recognition algorithms. Performance is measured in 

these competitions using standardized evaluation procedures applied to large sets of 

facial images (e.g., FERET, [4]; Face Recognition Vendor Test 2000, [2]; Face 

Recognition Vendor Test 2002, [5]; Face Recognition Grand Challenge, FRGC, [3]). 

The present work builds on the most recent of these large-scale tests of face 

recognition algorithms—the FRGC. The evaluation procedures used in this test provide a 

unique opportunity to benchmark human performance against current face recognition 

algorithms. This competition, initiated in 2004 and currently ongoing, is open to 

academic, industrial, and research lab competitors. The developers of computer-based 

face recognition systems volunteer their algorithms for evaluation on one or more face 

matching tasks varying in difficulty. 
 

 

3 

Comparing human and machine face recognition 

We carried out a direct comparison between humans and seven face recognition 

algorithms participating in the most difficult experiment of the FRGC: matching twodimensional face images taken under different illumination conditions (Fig. 1). The 

problem of face recognition over changes in illumination is widely recognized to be 

difficult for humans [7]-[11] and for algorithms [12], [13]. Illumination changes can vary 

the overall magnitude of light intensity reflected back from an object, as well as the 

pattern of shading and shadows visible in an image [14]. Indeed, varying the illumination 

can result in larger image differences than varying either the identity [15] or the 

viewpoint [14] of a face. These illumination changes have well-documented, detrimental 

effects on human accuracy recognizing faces (for a review, see [16]). 

For face recognition algorithms, the difficulty of the illumination problem is evident 

in comparing the FRGC experiments for matching controlled versus uncontrolledillumination image pairs. In the controlled illumination experiment of the FRGC, 

algorithms match the identity of faces in two pictures taken under similar, controlled 

illumination conditions. The 17 algorithms participating in the controlled illumination 

experiment, to date, have achieved a median verification rate of .91 at the .001 false 

acceptance rate—where verification rate is the proportion of matched pairs correctly 

judged to be of the same person and false acceptance rate is the proportion of mismatched 

pairs judged incorrectly to be of the same person. By contrast, there are only seven 

algorithms competing in the uncontrolled illumination experiment. These algorithms have 

achieved a median verification rate of .42 at a false acceptance rate of .001. The 

difficulties posed by variable illumination conditions, therefore, remain a significant 

challenge for automatic face recognition systems. 
 

 

4 

Comparing human and machine face recognition 

In the FRGC, algorithms must determine if two faces in a pair of images are of the 

same person or of different people. Algorithms in the FRGC uncontrolled illumination 

experiment are required to match identities in approximately 128 million pairs of faces. 

Specifically, the test data for this experiment consist of all possible pairs of 16028 

“target” faces and 8014 “probe” faces [13]. The target images were taken under 

controlled illumination conditions typical of those used for passport photographs and the 

probe images were taken under uncontrolled illumination conditions (e.g., in a corridor) 

(Fig. 1). 

Participating algorithms produce a 16028 x 8014 matrix of similarity scores for all 

possible face pairs. A similarity score represents the end result of an algorithm’s 

computations to establish the likelihood that the faces in the image pair are the “same” 

person. A higher similarity score indicates a greater likelihood that the two faces are the 

same person. The similarity score matrix is used to compute the algorithms’ “same 

person” versus “different person” judgments. Face pairs with similarity scores greater 

than or equal to a set criterion c are judged to be of the “same” person; pairs with 

similarity less than or equal to c are judged to be of “different” people. By varying the 

criterion c over the full range of similarity scores, a complete receiver operating 

characteristic (ROC) curve is generated. An ROC curve indicates performance level for 

all possible combinations of correct verification and false acceptance rate. The use of an 

ROC curve for characterizing the performance of algorithms makes it relatively 

straightforward to compare human face matching data to the algorithm data. 

Testing humans on 128 million pairs of facial images is impossible. We focused, 

therefore, on a sample of the “easiest” and “most difficult” face pairs with a sampling 
 

 

5 

Comparing human and machine face recognition 

procedure defined by a baseline algorithm. In three experiments, we tested human 

performance on “easy” and “difficult” face pairs, varying the exposure time of the face 

pairs from unlimited time to 500ms. Humans rated the likelihood that the two images 

were of the same person. Next, we extracted similarity scores from the seven algorithms 

on the same set of face pairs matched by the human participants. Finally, ROC curves 

were generated for the algorithms and humans. These form the primary basis of our 

comparisons between humans and machines on the face-matching task. 

a.) 

b.) 

Fig. 1 A sample pair of face images from a “match” trial (a) and a “ no match” trial (b). Participants responded by rating the 

likelihood that the pictures were of the same person using a five-point scale ranging from “1.) sure they are the same 

person” to “5.) sure they are not the same people.” 
 

 

6 

Comparing human and machine face recognition 

II. EXPERIMENTS 

A. Methods 

1) Stimuli 

Face stimuli were chosen from a large database developed for the FRCG study [13]. 

As noted, the uncontrolled illumination experiment from the FRCG used 8014 probe 

faces and 16028 target faces. The uncontrolled illumination probe faces had a resolution 

of 2272 x 1704 pixels. The controlled illumination target faces had a resolution of 1704 x 

2272 pixels. For these experiments, we sampled face pairs from 128,448,392 pairs 

available. Of these, 407,352 (0.32%) were of the same people (match pairs) and 

128,041,040 (99.68%) were of different people (non-match pairs, see Fig. 1 for an 

example pair). 

To make the task as challenging as possible, we narrowed the available pairs to 

include only Caucasian males and females. Restriction to this relatively homogeneous set 

of faces eliminates the possibility that algorithms or humans can base identity 

comparisons on surface facial characteristics associated with race or age. For the same 

reason, the face pairs presented to participants were matched by sex. 

Next we divided the face pairs into “easy” and “difficult.” To estimate pair difficulty, 

we used a control algorithm based on principal components analysis (PCA) of the aligned 

and scaled images. PCA algorithms are an appropriate baseline because they have been 

available and widely tested since the early 1990’s [17], [18]. The FRGC version of this 

baseline was designed to optimize performance [19]. We show that this algorithm 

reliably predicts “easy” and “difficult” sets of face pairs for both humans and algorithms. 

The algorithm itself, however, is not considered “state-of-the-art”. 
 

 

 

7 

Comparing human and machine face recognition 

The baseline algorithm generated a 16028 x 8014 similarity matrix. We defined 

difficult match pairs to be image pairs of the same person with similarity scores less than 

two standard deviations below the average similarity score for the same-person match 

pairs (i.e., highly dissimilar images of the same person). Easy match pairs were image 

pairs of the same person with similarity scores greater than two standard deviations above 

the mean similarity score for the same-person pairs (i.e., highly similar images of the 

same person). Difficult non-match pairs and easy non-match pairs were chosen 

analogously (e.g., image pairs of different faces with similarity scores greater/less than 

two standard deviations above/below the average similarity score for the different-person 

pairs). The face pairs used in the experiments were selected randomly from face pairs 

meeting the above criteria. 

2) Participants 

Students from the School of Behavioral and Brain Sciences at University of Texas at 

Dallas volunteered to participate in these experiments in exchange for a research credit in 

a psychology course. A total of 91 students participated in the experiments (Exp. 1, n = 

22, 10 females and 12 males; Exp. 2, n = 49, 25 females and 24 males; Exp. 3, n = 20, 10 

females and 10 males).

 3) Procedure 

For all experiments, the task was to determine whether two face images, which 

appeared side by side on a computer screen, were pictures of the same person or of 

different people. Probe images were displayed on the left and target images were 

displayed on the right. The participants were asked to rate the image pairs using a 1 to 5 
 

8 

Comparing human and machine face recognition 

scale, “1.) sure they are the same; 2.) think they are the same; 3.) don’t know; 4.) think 

they are not the same; and 5.) sure they are not the same.” 

In Experiment 1, 120 pairs of male faces served as stimuli. Half of the pairs were 

prescreened to be “easy” and half were prescreened to be “difficult”. Participants had 

unlimited time to enter a response for each pair, with images remaining on the screen 

until a response was entered. 

Experiment 2 was similar to Experiment 1, but with exposure limited to two seconds. 

In this experiment, we included an equal number of male (120) and female (120) face 

pairs and balanced the inclusion of male and female human participants. Again, half of 

the pairs were prescreened to be “easy” and half were prescreened to be “difficult”. The 

exposure time was reduced to two seconds with the purpose of increasing error rates. 

This exposure time reduction did not diminish human performance significantly, and so 

Experiment 3 was conducted. 

Experiment 3 was identical to Experiment 2, but with exposure time set to 500 

milliseconds. This reduced performance considerably. A pilot study, not reported here, 

showed that the error rates did not increase substantially from the unlimited time 

condition when the exposure time was reduced to 1 second. 

The algorithm-human comparisons we report in this study are based on data from 

Experiment 2. We chose this experiment for comparisons because we think that two 

seconds is a “realistic” exposure time for many security applications, and because the 

experiment includes a balanced number of male and female participants and male and 

female faces. 
 

 

9 

Comparing human and machine face recognition 

B. Results 

1) Behavioral Data. 

For the purposes of conducting inferential statistical analyses on the behavioral data, 

participant responses were transformed into “same” or “different” judgments for 

individual pairs of faces, so that d’ could be computed. Responses 1 and 2 were deemed 

“same” judgments and responses 3, 4, and 5 were deemed “different” judgments. The 

correct verification rate (i.e., hit rate) was computed as the proportion of matched pairs 

correctly judged to be of the same person. The false acceptance rate (i.e., false alarm rate) 

was calculated as the proportion of non-match pairs judged incorrectly to be of the same 

person. A d’ was then computed from the hit and false alarm rates as Zhit rate - Zfalse alarm rate. 

ROC curves were computed from the full range of response data that assigned certainty 

values to each match/no-match judgment. These curves provide analogous data to the 

ROCs computed from the algorithms, across the range of verification and false 

acceptance rates. The d’ values served as the dependent variable in the analysis of 

variance (ANOVA) results reported for each experiment. 

The difficulty level of face pairs (i.e., as defined by baseline control algorithm) was 

evaluated as a within-subjects variable in all three experiments. The effects of sex of the 

participant (between-subjects variable) and sex of face pair (within-subjects variable) 

were examined in Experiment 2, where both factors were closely balanced. No significant 

differences or interactions were found for these gender variables, and so we do not 

consider them further. 

The PCA algorithm’s ability to predict accuracy for humans was verified in all three 

face-matching experiments. Humans were significantly more accurate on face pairs 
 

10 

Comparing human and machine face recognition 

estimated by the PCA to be easy than they were on the face pairs estimated to be difficult 

(Fig. 2). This was true when people had unlimited time to match the pairs (Exp. 1, 

F(1,20)=19.78, p < .0002); when the pairs were presented for two-seconds (Exp. 2, 

F(1,48)=96.53, p < .0001); and when the pairs were presented for 500 milliseconds 

(Exp. 3, F(1,18)=62.65, p < .0001). Performance on the face-matching task was good, 

but not perfect (see Fig. 2, for the Exp. 2 results). Remarkably, human participants 

performed no better with unlimited time to examine each pair than with a 2-second time 

limit (F(1,176) = 1.65, ns.). Match accuracy declined, however, when exposure time was 

limited to 500 milliseconds (F(1,176) = 22.37, p < .0001). 

Human Face Matching 

0 

0.2 

0.4 

0.6 

0.8 

1 

verification rate 

difficult pairs 

easy face pairs 

0 0.2 0.4 0.6 0.8 1 

false accept rate 

Fig. 2. The accuracy of humans matching face identities for “easy” versus “difficult” face pairs, with easy/difficult estimated 

by the baseline PCA algorithm. Face pairs found to be more difficult for the PCA were likewise more difficult for humans. 

Combining across the three experiments, two findings are worth noting. First, human 

face matching accuracy was roughly constant for exposure times varying from two 

seconds to “unlimited” time. Only in Experiment 3, where exposure time was reduced to 
 

 

 

 

11 

Comparing human and machine face recognition 

500ms, was there a substantial decline in performance. This suggests that more time, or 

the use of a more analytic and time-consuming strategy by humans, would not have 

pushed performance levels to perfection. Whatever strategy humans employ to reach 

these “good” to “excellent” levels of performance seems to operate quickly and 

efficiently. The relatively stable performance of humans across the exposure times 

supports the use of these data as meaningful benchmark for algorithm performance. 

A second result is that PCA can serve as an effective tool for pre-screening easy 

versus difficult face pairs for humans. 

2) Human-Algorithm Comparison 

We compared human performance in Experiment 2 to the performance of seven 

algorithms participating in the illumination experiment of the FRGC. Three of these 

algorithms were developed at academic institutions and four were developed by 

commercial enterprises. 

As noted, each participating algorithm produced a 16028 x 8014 matrix of similarity 

scores for all the face pairs. The similarity scores of the 240 (120 male and 120 female) 

face pairs presented to participants in Experiment 2 were extracted from each similarity 

matrix and analyzed as follows. A full ROC curve was generated for each algorithm by 

sweeping a match criterion, c, across the distributions of “match” and “no-match” 

similarity scores and assigning same or different responses for individual face pairs, (e.g., 

similarity score greater than c yielded “same” and similarity score less than or equal to c 

yielded “different”). The verification and the false acceptance rates were used to compute 

ROC curves for the algorithms. 
 

12 

Comparing human and machine face recognition 

Figure 3a shows the performance of the algorithms and humans on the difficult face 

pairs. Three algorithms were more accurate than humans at this task and four algorithms 

were less accurate than humans. Two of the three algorithms that surpassed human 

performance were developed at academic institutions [20],[21]. The third algorithm is 

from industry and is described partially in a recent paper [22]. 

The performance of algorithms relative to humans for the easy face pairs was even 

better. Six of the seven algorithms performed more accurately than humans (Fig. 3b). The 

seventh algorithm exceeded human performance only at low false acceptance rates. 

In all cases, algorithm performance was better for the “easy” face pairs than for the 

“difficult” face pairs, echoing the pattern for humans. 

Identity Matching for Difficult Face Pairs Identity Matching for Easy Face Pairs 

0 

0.2 

0.4 

0.6 

0.8 

1 

Verification Rate

NJIT 

CMU 

Viisage 

Human Performance 

Algorithm A 

Algorithm B 

Algorithm C 

Algorithm D 

Chance Performance 

0 

0.2 

0.4 

0.6 

0.8 

1 

Verification Rate

CMU 

NJIT 

Algorithm B 

Algorithm D 

Viisage 

Algorithm C 

Algorithm A 

Human Performance 

Chance Performance 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

False Accept Rate False Accept Rate 

Fig. 3. Performance of humans and seven algorithms on the difficult face pairs (Fig. 3a) and easy face pairs (Fig. 3b) shown with ROC 

curves. Three algorithms outperform humans on the difficult face pairs at most or all combinations of verification rate and false accept 

rate (cf., [20] NJIT, [21] CMU for details on two of the three algorithms). Humans out-perform the other four algorithms on the difficult 

face pairs. All but one algorithm performs more accurately than humans on the easy face pairs. 

Did human performance suffer from fatigue or waning attention over the course of 

the experiment? To examine this possibility, we assessed human performance over the 
 

 

 

 

 

13 

Comparing human and machine face recognition 

sequence of face pairs presented. Accuracy did not vary with trial number for either the 

verification rate (r = .07, ns.) or for the false acceptance rate (r = -.04, ns.). It is unlikely, 

therefore, that the comparatively lower performance of humans versus algorithms on the 

easy face pairs was due to difficulties in maintaining attention for the duration of the 

experiment. 

III. GENERAL DISCUSSION 

There is an implicit assumption among computer vision researchers, psychologists, and 

indeed much of the general public that human abilities recognizing and matching faces 

are currently beyond reach for machines. The present experiments challenge this 

assumption by showing that some current face recognition algorithms can compete with 

humans on a challenging task—matching face identity between photographs that are 

taken under different illumination conditions. Although algorithm performance may not 

seem impressive in absolute terms, it nonetheless compares favorably with humans. 

The comparisons we report may lead us to wonder if human abilities with faces are 

overrated. Before concluding that they are, we note that human participants in these 

experiments were asked to match the faces of people previously unknown to them. This 

is an appropriate task for testing the abilities of a human security guard and/or algorithm, 

but may not show human face recognition skills at their best. The robust recognition 

abilities that characterize human expertise for faces may be limited to the faces of people 

we know well. Indeed, by contrast to performance with unfamiliar faces, human face 

recognition abilities for familiar faces are relatively robust to changes in viewing 

parameters such as illumination and pose [23], [24]. 
 

14 

Comparing human and machine face recognition 

The consistent advantage of the algorithms over humans on the “easy” faces suggests 

that easy face pairs may simply be those in which the image-based features for the two 

faces are well matched and the illumination differences between images are minimal. 

Because image-based matching is a task that face recognition algorithms have done well 

for many years [1], it is perhaps not surprising that algorithms can compete with humans 

on this task. The algorithms might exploit useful, but subtle, image-based information 

that give them a slight, but consistent, advantage over humans. 

An explanation for the better performance of algorithms on the difficult face pairs is 

less clear and may await more information and further tests of the algorithms that achieve 

this better performance. Although a discussion of these algorithms is beyond the scope 

of this paper, suffice to say that the Liu [20] and Xie et al. [21] algorithms represent a 

departure from past approaches. Both algorithms use kernel methods and both make 

efficient use of multiple “training” images of individuals to create a face feature space. 

The training images were made available to algorithm developers for “optional use,” and 

were not included in the target or probe sets. The Liu [20] and Xie et al. [21] algorithms 

represent faces in the derived face feature space before matching by identity. It is 

possible that these algorithms work well because of information they can exploit from 

training images about the variability of individuals across changes in illumination. This 

may be similar to information humans acquire in developing face recognition skills and 

in becoming familiar with individuals. 

The power of combining variable face images to improve face recognition has been 

explored also by Burton, Jenkins, Hancock, and White [25]. They used a technique based 

on averaging multiple aligned face images of the same person and found that it 
 

15 

Comparing human and machine face recognition 

outperformed comparable systems based on collections of instances. They demonstrated 

also that the averaging procedure produced faces that humans recognized more accurately 

than the original face images. Algorithms that make coordinated use of multiple images, 

therefore, can potentially offer insight into the question of how familiarity with multiple 

images of individual faces may help to buffer human and machine recognition against 

spurious changes in viewing parameters. 

Finally, we tend to accept at “face” value, the pressing need to evaluate the 

performance of any algorithm that is placed in the field for a security application of 

importance. The tools and techniques for evaluating human face matching and 

recognition performance have been available for many years. These tools make it 

possible to test humans before assigning them to security tasks. This is an important, even 

critical, step for evaluating the level of security provided by human operators, by 

machine operators, and by the human-machine partnerships that will likely become 

commonplace in the near future for many security applications. 

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Comparing human and machine face recognition 

ACKKNOWLEGEMENTS 

The primary goal of the FRGC is to encourage and facilitate the development of face 

recognition algorithms. To provide the face recognition research community with an 

unbiased assessment of state-of-the-art algorithms, research groups voluntarily submit 

similarity scores from prototyped experiments to the National Institute of Standards and 

Technology (NIST) for analysis. The results of the analysis by NIST are anonymous, 

unless otherwise agreed to by the participating algorithm developers. All participating 

groups were given the choice of remaining anonymous or being identified in this report. 

Performance results are from Jan. 2005 for all algorithms except Xie et al., 2005, where 

results are from Aug., 2005. This work was supported by a contract to A. O’Toole and 

H. Abdi from TSWG. P. J. Phillips was supported in part by funding from the National 

Institute of Justice. The identification of any commercial product or trade name does not 

imply endorsement or recommendation by NIST.