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Real-Time Eye Blink Detection using Facial Landmarks.pdf

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    21st Computer Vision Winter Workshop Luka ˇCehovin, Rok Mandeljc, Vitomir ˇStruc (eds.) Rimske Toplice, Slovenia, February 3–5, 2016 Real-Time Eye Blink Detection using Facial Landmarks Center for Machine Perception, Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University in Prague Tereza Soukupov´a and Jan ˇCech {soukuter,cechj}@cmp.felk.cvut.cz Abstract. A real-time algorithm to detect eye blinks in a video sequence from a standard camera is pro- posed. Recent landmark detectors, trained on in-the- wild datasets exhibit excellent robustness against a head orientation with respect to a camera, varying illumination and facial expressions. We show that the landmarks are detected precisely enough to reli- ably estimate the level of the eye opening. The pro- posed algorithm therefore estimates the landmark positions, extracts a single scalar quantity – eye as- pect ratio (EAR) – characterizing the eye opening in each frame. Finally, an SVM classifier detects eye blinks as a pattern of EAR values in a short tempo- ral window. The simple algorithm outperforms the state-of-the-art results on two standard datasets. 1. Introduction Detecting eye blinks is important for instance in systems that monitor a human operator vigilance, e.g. driver drowsiness [5, 13], in systems that warn a computer user staring at the screen without blink- ing for a long time to prevent the dry eye and the computer vision syndromes [17, 7, 8], in human- computer interfaces that ease communication for dis- abled people [15], or for anti-spoofing protection in face recognition systems [11]. Existing methods are either active or passive. Ac- tive methods are reliable but use special hardware, often expensive and intrusive, e.g. infrared cameras and illuminators [2], wearable devices, glasses with a special close-up cameras observing the eyes [10]. While the passive systems rely on a standard remote camera only. Many methods have been proposed to automati- cally detect eye blinks in a video sequence. Several methods are based on a motion estimation in the eye region. Typically, the face and eyes are detected by Figure 1: Open and closed eyes with landmarks pi automatically detected by [1]. The eye aspect ratio EAR in Eq. (1) plotted for several frames of a video sequence. A single blink is present. a Viola-Jones type detector. Next, motion in the eye area is estimated from optical flow, by sparse track- ing [7, 8], or by frame-to-frame intensity differenc- ing and adaptive thresholding. Finally, a decision is made whether the eyes are or are not covered by eye- lids [9, 15]. A different approach is to infer the state of the eye opening from a single image, as e.g. by correlation matching with open and closed eye tem- plates [4], a heuristic horizontal or vertical image in- tensity projection over the eye region [5, 6], a para- metric model fitting to find the eyelids [18], or active shape models [14]. A major drawback of the previous approaches is that they usually implicitly impose too strong re- quirements on the setup, in the sense of a relative face-camera pose (head orientation), image resolu- tion, illumination, motion dynamics, etc. Especially the heuristic methods that use raw image intensity are likely to be very sensitive despite their real-time performance.
    However nowadays, robust real-time facial land- mark detectors that capture most of the character- istic points on a human face image, including eye corners and eyelids, are available, see Fig. 1. Most of the state-of-the-art landmark detectors formulate a regression problem, where a mapping from an im- age into landmark positions [16] or into other land- mark parametrization [1] is learned. These mod- ern landmark detectors are trained on “in-the-wild datasets” and they are thus robust to varying illu- mination, various facial expressions, and moderate non-frontal head rotations. An average error of the landmark localization of a state-of-the-art detector is usually below five percent of the inter-ocular dis- tance. Recent methods run even significantly super real-time [12]. Therefore, we propose a simple but efficient al- gorithm to detect eye blinks by using a recent facial landmark detector. A single scalar quantity that re- flects a level of the eye opening is derived from the landmarks. Finally, having a per-frame sequence of the eye opening estimates, the eye blinks are found by an SVM classifier that is trained on examples of blinking and non-blinking patterns. Facial segmentation model presented in [14] is similar to the proposed method. However, their sys- tem is based on active shape models with reported processing time of about 5 seconds per frame for the segmentation, and the eye opening signal is normal- ized by statistics estimated by observing a longer se- quence. The system is thus usable for offline pro- cessing only. The proposed algorithm runs real-time, since the extra costs of the eye opening from land- marks and the linear SVM are negligible. The contributions of the paper are: 1. Ability of two state-of-the-art landmark de- tectors [1, 16] to reliably distinguish between the open and closed eye states is quantita- tively demonstrated on a challenging in-the- wild dataset and for various face image resolu- tions. 2. A novel real-time eye blink detection algorithm which integrates a landmark detector and a clas- sifier is proposed. The evaluation is done on two standard datasets [11, 8] achieving state-of-the- art results. The rest of the paper is structured as follows: The algorithm is detailed in Sec. 2, experimental valida- Figure 2: Example of detected blinks. The plots of the eye aspect ratio EAR in Eq. (1), results of the EAR thresholding (threshold set to 0.2), the blinks detected by EAR SVM and the ground-truth labels over the video sequence. Input image with detected landmarks (depicted frame is marked by a red line). tion and evaluation is presented in Sec. 3. Finally, Sec. 4 concludes the paper. 2. Proposed method The eye blink is a fast closing and reopening of a human eye. Each individual has a little bit different pattern of blinks. The pattern differs in the speed of closing and opening, a degree of squeezing the eye and in a blink duration. The eye blink lasts approxi- Eye aspect ratio:0.40.20EAR thresholding (t = 0.2):EAR SVM output:Ground-truth:blinkblinkblinknon-blinknon-blinknon-blinkhalf
    mately 100-400 ms. We propose to exploit state-of-the-art facial land- mark detectors to localize the eyes and eyelid con- tours. From the landmarks detected in the image, we derive the eye aspect ratio (EAR) that is used as an estimate of the eye opening state. Since the per- frame EAR may not necessarily recognize the eye blinks correctly, a classifier that takes a larger tem- poral window of a frame into account is trained. 2.1. Description of features For every video frame, the eye landmarks are de- tected. The eye aspect ratio (EAR) between height and width of the eye is computed. EAR = p2 − p6 + p3 − p5 2p1 − p4 , (1) where p1, . . . , p6 are the 2D landmark locations, de- picted in Fig. 1. The EAR is mostly constant when an eye is open and is getting close to zero while closing an eye. It is partially person and head pose insensitive. Aspect ratio of the open eye has a small variance among indi- viduals and it is fully invariant to a uniform scaling of the image and in-plane rotation of the face. Since eye blinking is performed by both eyes synchronously, the EAR of both eyes is averaged. An example of an EAR signal over the video sequence is shown in Fig. 1, 2, 7. A similar feature to measure the eye opening was suggested in [9], but it was derived from the eye seg- mentation in a binary image. 2.2. Classification It generally does not hold that low value of the EAR means that a person is blinking. A low value of the EAR may occur when a subject closes his/her eyes intentionally for a longer time or performs a fa- cial expression, yawning, etc., or the EAR captures a short random fluctuation of the landmarks. Therefore, we propose a classifier that takes a larger temporal window of a frame as an input. For the 30fps videos, we experimentally found that ±6 frames can have a significant impact on a blink detec- tion for a frame where an eye is the most closed when blinking. Thus, for each frame, a 13-dimensional feature is gathered by concatenating the EARs of its ±6 neighboring frames. This is implemented by a linear SVM classifier (called EAR SVM) trained from manually anno- tated sequences. Positive examples are collected as ground-truth blinks, while the negatives are those that are sampled from parts of the videos where no blink occurs, with 5 frames spacing and 7 frames margin from the ground-truth blinks. While testing, a classifier is executed in a scanning-window fashion. A 13-dimensional feature is computed and classified by EAR SVM for each frame except the beginning and ending of a video sequence. 3. Experiments Two types of experiments were carried out: The experiments that measure accuracy of the landmark detectors, see Sec. 3.1, and the experiments that eval- uate performance of the whole eye blink detection algorithm, see Sec 3.2. 3.1. Accuracy of landmark detectors To evaluate accuracy of tested landmark detectors, we used the 300-VW dataset [19]. It is a dataset con- taining 50 videos where each frame has associated a precise annotation of facial landmarks. The videos are “in-the-wild”, mostly recorded from a TV. The purpose of the following tests is to demon- strate that recent landmark detectors are particularly robust and precise in detecting eyes, i.e. the eye- corners and contour of the eyelids. Therefore we pre- pared a dataset, a subset of the 300-VW, containing sample images with both open and closed eyes. More precisely, having the ground-truth landmark annota- tion, we sorted the frames for each subject by the eye aspect ratio (EAR in Eq. (1)) and took 10 frames of the highest ratio (eyes wide open), 10 frames of the lowest ratio (mostly eyes tightly shut) and 10 frames sampled randomly. Thus we collected 1500 images. Moreover, all the images were later subsampled (suc- cessively 10 times by factor 0.75) in order to evaluate accuracy of tested detectors on small face images. Two state-of-the-art landmark detectors were tested: Chehra [1] and Intraface [16]. Both run in real-time1. Samples from the dataset are shown in Fig. 3. Notice that faces are not always frontal to the camera, the expression is not always neutral, peo- ple are often emotionally speaking or smiling, etc. Sometimes people wear glasses, hair may occasion- ally partially occlude one of the eyes. Both detectors perform generally well, but the Intraface is more ro- bust to very small face images, sometimes at impres- sive extent as shown in Fig. 3. 1Intraface runs in 50 Hz on a standard laptop.
    Figure 3: Example images from the 300-VW dataset with landmarks obtained by Chehra [1] and In- traface [16]. Original images (left) with inter-ocular distance (IOD) equal to 63 (top) and 53 (bottom) pix- els. Images subsampled (right) to IOD equal to 6.3 (top) and 17 (bottom). Quantitatively, the accuracy of the landmark de- tection for a face image is measured by the average relative landmark localization error, defined as usu- ally N i=1  = 100 κN ||xi − ˆxi||2, (2) where xi is the ground-truth location of landmark i in the image, ˆxi is an estimated landmark location by a detector, N is a number of landmarks and normal- ization factor κ is the inter-ocular distance (IOD), i.e. Euclidean distance between eye centers in the image. First, a standard cumulative histogram of the aver- age relative landmark localization error  was calcu- lated, see Fig. 4, for a complete set of 49 landmarks and also for a subset of 12 landmarks of the eyes only, since these landmarks are used in the proposed eye blink detector. The results are calculated for all the original images that have average IOD around 80 px, and also for all “small” face images (including sub- sampled ones) having IOD ≤ 50 px. For all land- marks, Chehra has more occurrences of very small errors (up to 5 percent of the IOD), but Intraface is more robust having more occurrences of errors be- low 10 percent of the IOD. For eye landmarks only, Figure 4: Cumulative histogram of average localiza- tion error of all 49 landmarks (top) and 12 landmarks of the eyes (bottom). The histograms are computed for original resolution images (solid lines) and a sub- set of small images (IOD ≤ 50 px). the Intraface is always more precise than Chehra. As already mentioned, the Intraface is much more robust to small images than Chehra. This behaviour is fur- ther observed in the following experiment. Taking a set of all 15k images, we measured a mean localization error µ as a function of a face im- age resolution determined by the IOD. More pre- cisely, µ = 1|S| j∈S j, i.e. average error over set of face images S having the IOD in a given range. Re- sults are shown in Fig. 5. Plots have errorbars of stan- dard deviation. It is seen that Chehra fails quickly for images with IOD < 20 px. For larger faces, the mean error is comparable, although slightly better for Intraface for the eye landmarks. The last test is directly related to the eye blink de- tector. We measured accuracy of EAR as a func- tion of the IOD. Mean EAR error is defined as a mean absolute difference between the true and the estimated EAR. The plots are computed for two sub- sets: closed/closing (average true ratio 0.05 ± 0.05) ChehraIntraface ChehraIntraface ChehraIntraface ChehraIntraface0510152025020406080100localization error [% of IOD]occurance [%]All landmarks ChehraIntrafaceChehra−smallIntraface−small0510152025020406080100localization error [% of IOD]occurance [%]Eye landmarks ChehraIntrafaceChehra−smallIntraface−small
    Figure 5: Landmark localization accuracy as a func- tion of the face image resolution computed for all landmarks and eye landmarks only. and open eyes (average true ratio 0.4 ± 0.1). The error is higher for closed eyes. The reason is prob- ably that both detectors are more likely to output open eyes in case of a failure. It is seen that ratio error for IOD < 20 px causes a major confusion between open/close eye states for Chehra, neverthe- less for larger faces the ratio is estimated precisely enough to ensure a reliable eye blink detection. 3.2. Eye blink detector evaluation We evaluate on two standard databases with ground-truth annotations of blinks. The first one is ZJU [11] consisting of 80 short videos of 20 sub- jects. Each subject has 4 videos: 2 with and 2 without glasses, 3 videos are frontal and 1 is an upward view. The 30fps videos are of size 320 × 240 px. An av- erage video length is 136 frames and contains about 3.6 blinks in average. An average IOD is 57.4 pixels. In this database, subjects do not perform any notice- able facial expressions. They look straight into the camera at close distance, almost do not move, do not Figure 6: Accuracy of the eye-opening ratio as a function of the face image resolution. Top: for images with small true ratio (mostly closing/closed eyes), and bottom: images with higher ratio (open eyes). either smile nor speak. A ground-truth blink is de- fined by its beginning frame, peak frame and ending frame. The second database Eyeblink8 [8] is more challenging. It consists of 8 long videos of 4 sub- jects that are smiling, rotating head naturally, cover- ing face with hands, yawning, drinking and looking down probably on a keyboard. These videos have length from 5k to 11k frames, also 30fps, with a res- olution 640 × 480 pixels and an average IOD 62.9 pixels. They contain about 50 blinks on average per video. Each frame belonging to a blink is annotated by half-open or close state of the eyes. We consider half blinks, which do not achieve the close state, as full blinks to be consistent with the ZJU. Besides testing the proposed EAR SVM methods, that are trained to detect the specific blink pattern, we compare with a simple baseline method, which only thresholds the EAR in Eq. (1) values. The EAR SVM classifiers are tested with both landmark detec- tors Chehra [1] and Intraface [16]. 02040608010001020304050IOD [px]mean error [% of IOD]All landmarks ChehraIntraface02040608010001020304050IOD [px]mean error [% of IOD]Eye landmarks ChehraIntraface02040608010000.050.10.150.20.250.30.350.4IOD [px]mean eye opening errorLow opening ratio (ρ < 0.15) ChehraIntraface02040608010000.050.10.150.20.250.30.350.4IOD [px]mean eye opening errorHigh opening ratio (ρ > 0.25) ChehraIntraface
    section with detected blinks. The number of false negatives is counted as a number of the ground-truth blinks which do not intersect detected blinks. The number of false positives is equal to the number of detected blinks minus the number of true positives plus a penalty for detecting too long blinks. The penalty is counted only for detecting blinks twice longer then an average blink of length A. Every long blink of length L is counted L A times as a false posi- tive. The number of all possibly detectable blinks is computed as number of frames of a video sequence divided by subject average blink length following Drutarovsky and Fogelton [8]. The ZJU database appears relatively easy. It mostly holds that every eye closing is an eye blink. Consequently, the precision-recall curves shown in Fig. 8a of the EAR thresholding and both EAR SVM classifiers are almost identical. These curves were calculated by spanning a threshold of the EAR and SVM output score respectively. All our methods outperform other detectors [9, 8, 5]. The published methods presented the precision and the recall for a single operation point only, not the precision-recall curve. See Fig. 8a for comparison. The precision-recall curves in Fig. 8b shows eval- uation on the Eyeblink8 database. We observe that in this challenging database the EAR thresholding lags behind both EAR SVM classifiers. The thresholding fails when a subject smiles (has narrowed eyes - see an example in Fig. 7), has a side view or when the subject closes his/her eyes for a time longer than a blink duration. Both SVM detectors performs much better, the Intraface detector based SVM is even a little better than the Chehra SVM. Both EAR SVM detectors outperform the method by Drutarovsky and Fogelton [8] by a significant margin. Finally, we measured a dependence of the whole blink detector accuracy on the average IOD over the dataset. Every frame of the ZJU database was sub- sampled to 90%, 80%, ..., 10% of its original reso- lution. Both Chehra-SVM and Intraface-SVM were used for evaluation. For each resolution, the area un- der the precision-recall curve (AUC) was computed. The result is shown in Fig. 9. We can see that with Chehra landmarks the accuracy remains very high until average IOD is about 30 px. The detector fails on images with the IOD < 20 px. Intraface land- marks are much better in low resolutions. This con- firms our previous study on the accuracy of land- marks in Sec. 3.1. Figure 7: Example of detected blinks where the EAR thresholding fails while EAR SVM succeeds. The plots of the eye aspect ratio EAR in Eq. (1), re- sults of the EAR thresholding (threshold set to 0.2), the blinks detected by EAR SVM and the ground- truth labels over the video sequence. Input image with detected landmarks (depicted frame is marked by a red line). The experiment with EAR SVM is done in a cross- dataset fashion. It means that the SVM classifier is trained on the Eyeblink8 and tested on the ZJU and vice versa. To evaluate detector accuracy, predicted blinks are compared with the ground-truth blinks. The number of true positives is determined as a number of the ground-truth blinks which have a non-empty inter- Eye aspect ratio:EAR thresholding (t = 0.2)EAR SVM output:Ground-truth:blinknon-blinkhalfnon-blinkblinknon-blinkblink00.20.4
    (a) ZJU (b) Eyeblink8 Figure 8: Precision-recall curves of the EAR thresh- olding and EAR SVM classifiers measured on (a) the ZJU and (b) the Eyeblink8 databases. Published re- sults of methods A - Drutarovsky and Fogelton [8], B - Lee et al. [9], C - Danisman et al. [5] are depicted. 4. Conclusion A real-time eye blink detection algorithm was presented. We quantitatively demonstrated that regression-based facial landmark detectors are pre- cise enough to reliably estimate a level of eye open- ness. While they are robust to low image quality (low image resolution in a large extent) and in-the-wild Figure 9: Accuracy of the eye blink detector (mea- sured by AUC) as a function of the image resolution (average IOD) when subsampling the ZJU dataset. phenomena as non-frontality, bad illumination, facial expressions, etc. State-of-the-art on two standard datasets was achieved using the robust landmark detector fol- lowed by a simple eye blink detection based on the SVM. The algorithm runs in real-time, since the ad- ditional computational costs for the eye blink detec- tion are negligible besides the real-time landmark de- tectors. The proposed SVM method that uses a temporal window of the eye aspect ratio (EAR), outperforms the EAR thresholding. On the other hand, the thresh- olding is usable as a single image classifier to detect the eye state, in case that a longer sequence is not available. We see a limitation that a fixed blink duration for all subjects was assumed, although everyone’s blink lasts differently. The results could be improved by an adaptive approach. Another limitation is in the eye opening estimate. While EAR is estimated from a 2D image, it is fairly insensitive to a head orientation, but may lose discriminability for out of plane rotations. A solution might be to define the EAR in 3D. There are landmark detectors that estimate a 3D pose (po- sition and orientation) of a 3D model of landmarks, e.g. [1, 3]. Acknowledgment The research was supported by CTU student grant SGS15/155/OHK3/2T/13. 01020304050607080901000102030405060708090100BACRecall [%]Precision [%] EAR ThresholdingChehra SVMIntraface SVM01020304050607080901000102030405060708090100ARecall [%]Precision [%] EAR ThresholdingChehra SVMIntraface SVM57.3851.645.940.234.428.723.017.211.55.700.10.20.30.40.50.60.70.80.91IOD [px]AUC Chehra SVMIntraface SVM
    [16] X. Xiong and F. De la Torre. Supervised descent methods and its applications to face alignment. In Proc. CVPR, 2013. 2, 3, 4, 5 [17] Z. Yan, L. Hu, H. Chen, and F. Lu. Computer vision syndrome: A widely spreading but largely unknown epidemic among computer users. Computers in Hu- man Behaviour, (24):2026–2042, 2008. 1 [18] F. Yang, X. Yu, J. Huang, P. Yang, and D. Metaxas. Robust eyelid tracking for fatigue detection. In ICIP, 2012. 1 [19] S. Zafeiriou, G. Tzimiropoulos, and M. Pantic. The 300 videos in the wild (300-VW) facial landmark In ICCV Work- tracking in-the-wild challenge. shop, 2015. http://ibug.doc.ic.ac.uk/ resources/300-VW/. 3 References [1] A. Asthana, S. Zafeoriou, S. Cheng, and M. Pantic. Incremental face alignment in the wild. In Confer- ence on Computer Vision and Pattern Recognition, 2014. 1, 2, 3, 4, 5, 7 [2] L. M. Bergasa, J. Nuevo, M. A. Sotelo, and M. Vazquez. Real-time system for monitoring driver vigilance. In IEEE Intelligent Vehicles Symposium, 2004. 1 [3] J. Cech, V. Franc, and J. Matas. A 3D approach to facial landmarks: Detection, refinement, and track- In Proc. International Conference on Pattern ing. Recognition, 2014. 7 [4] M. Chau and M. Betke. Real time eye tracking and blink detection with USB cameras. Technical Report 2005-12, Boston University Computer Science, May 2005. 1 [5] T. Danisman, I. Bilasco, C. Djeraba, and N. Ihad- dadene. Drowsy driver detection system using eye In Machine and Web Intelligence blink patterns. (ICMWI), Oct 2010. 1, 6, 7 [6] H. Dinh, E. Jovanov, and R. Adhami. Eye blink detection using intensity vertical projection. In In- ternational Multi-Conference on Engineering and Technological Innovation, IMETI 2012. 1 [7] M. Divjak and H. Bischof. Eye blink based fa- tigue detection for prevention of computer vision syndrome. In IAPR Conference on Machine Vision Applications, 2009. 1 [8] T. Drutarovsky and A. Fogelton. Eye blink detec- tion using variance of motion vectors. In Computer Vision - ECCV Workshops. 2014. 1, 2, 5, 6, 7 [9] W. H. Lee, E. C. Lee, and K. E. Park. Blink detec- tion robust to various facial poses. Journal of Neu- roscience Methods, Nov. 2010. 1, 3, 6, 7 [10] Medicton group. The system I4Control. http:// www.i4tracking.cz/. 1 [11] G. Pan, L. Sun, Z. Wu, and S. Lao. Eyeblink-based anti-spoofing in face recognition from a generic we- bcamera. In ICCV, 2007. 1, 2, 5 [12] S. Ren, X. Cao, Y. Wei, and J. Sun. Face alignment at 3000 fps via regressing local binary features. In Proc. CVPR, 2014. 2 [13] A. Sahayadhas, K. Sundaraj, and M. Murugappan. Detecting driver drowsiness based on sensors: A re- view. MDPI open access: sensors, 2012. 1 [14] F. M. Sukno, S.-K. Pavani, C. Butakoff, and A. F. Frangi. Automatic assessment of eye blinking pat- In ICVS, terns through statistical shape models. 2009. 1, 2 [15] D. Torricelli, M. Goffredo, S. Conforto, and M. Schmid. An adaptive blink detector to initial- ize and update a view-basedremote eye gaze track- ing system in a natural scenario. Pattern Recogn. Lett., 30(12):1144–1150, Sept. 2009. 1
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