People v. Garner
Cases that cite this one
4 later published cases cite this decision.
- People v. McGhee (Appellate Division of the Supreme Court of New York 2021)
- People v. Johnson (Appellate Division of the Supreme Court of New York 2021)
- People v. Azor (Appellate Division of the Supreme Court of New York 2014)
- People v. Azor (Appellate Division of the Supreme Court of New York 2014)
This list shows which later cases cite this one. It does not say how they treated it, and no review of that has been done. Not a substitute for Shepard’s or KeyCite — verify before relying.
Opinion of the Court
Judgment, Supreme Court, New York County (Michael R. Ambrecht, J., at suppression hearing; Arlene R. Silverman, J., at plea and sentence), rendered September 11, 2007, convicting defendant of criminal sale of a controlled substance in the third degree, and sentencing him, as a second felony drug offender, to a term of 3V2 years, unanimously affirmed.
The court properly denied defendant’s motion to suppress identification testimony. As part of a long-term operation, an undercover officer made a drug buy from defendant and identified him from a photo array six weeks later, and from a lineup two months after that. Neither procedure was unduly suggestive (see People v Chipp, 75 NY2d 327, 336 [1990], cert denied 498 US 833 [1990]). In each procedure, the participants were generally similar in appearance and, given the particular description given by the undercover officer, there was nothing that highlighted defendant (see e.g. People v Carroll, 303 AD2d 200, 201 [2003], lv denied 100 NY2d 560 [2003]). To the extent the photo array could be viewed as suggestive, that suggestiveness was attenuated by the passage of time between the two procedures. Although the undercover officer received a copy of the photo array, there is no evidence that this influenced his lineup identification of defendant. Concur—Mazzarelli, J.P., Saxe, Moskowitz, Acosta and Renwick, JJ.
Case-law data current through December 31, 2025. Source: CourtListener bulk data.